Optimization of uprooting efficiency of counter-rotating cotton stalk puller for on-field operations

Optimization of uprooting efficiency of counter-rotating cotton stalk puller for on-field operations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimization of uprooting efficiency of counter-rotating cotton stalk puller for on-field operations Ashutosh Pandirwar, HIMANSHU Pandey, AJIT P Magar, AJAY K Roul, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4874230/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cotton stalks, a by-product left after cotton picking, have several industrial applications as a raw material. However, due to deep taproot system, the uprooting and disposal of cotton stalks from the field is a labour-intensive operation. In this study, the uprooting efficiency of a counter-rotating drum type cotton stalk puller (CSP) was optimized using Response Surface Methodology (RSM) and combined Artificial Neural Network (ANN) - Particle Swarm Optimization (PSO) approach. Machine operational parameters and design parameter were independent variables, whereas, uprooting efficiency, plants broken and plants left were response variables. Results An experimental CSP unit was operated in field at three forward speeds (1.37, 1.67 and 1.95 km/h), four drum speeds (250, 300, 350 and 400 rpm) and three drum inclinations (0 0 , 10 0 , 20 0 ). The optimization using RSM shown 332.5 rpm drum speed, 8.36 0 drum inclination and 1.37 km/h forward speed as optimal values. Plants uprooted, plants broken and plants left have optimum values of 96.6%, 2.8% and 1.1% with individual desirability of 0.97, 0.85 and 0.89 showing the closeness of responses to predicted values. ANN-PSO model shown optimal parameters as 1.37 km/h forward speed, 7.89 0 drum inclination and 331.45 rpm drum speed with the observed and predicted values of uprooting efficiency are 96.72% and 94.84%, respectively. Conclusion The results show that both RSM and combined ANN-PSO approach can better predict and optimize the performance of CSP with higher accuracy. Optimization study provide essential information on optimal combination of operating and design parameters for enhanced uprooting efficiency with minimum plant breakage. Cotton stalk Cotton mechanization Uprooting efficiency Stalk pulled Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Figure 23 Introduction Cotton (Gossypium L.) is a main fiber and oil seed crop having global importance, also more importantly a topic of significant scientific interest (Wendel and Grover, 2015 ; Meng et al., 2023 ). Cotton is mainly grown for fibre in more than seventy countries of the world of which China, India, USA, Brazil, and Australia are leading one accounting for approximately three-quarters of the cotton production of world during year 2022-23 (ICAC, 2023). Worldwide, cotton is cultivated in an area of 31.43 million ha with a production of 25.18 million tonnes (148.18 million bales) during year 2021–2022 (FAOSTAT, 2022). India has the largest area of 12.47 million ha and highest production of 32.31 million bales (170 kg per bale) among the cotton growing countries followed by China, United States and Pakistan (CCI, 2024; DES, 2024). High production of cotton is accompanied by generation of tonnes of cotton residues every year (Pandirwar et al., 2023a ). About 23–30 million tonnes of cotton residue is produced in India every year at an average rate of 3 tonnes per hectare of area (Ramanjaneyulu et al., 2021 ). Global cotton residues availability would be estimated between 90.3 and 129 million tonnes annually at a current cotton production rate and is projected to grow consequently (Fawzy et al., 2021 ). In most part of the world, this invaluable biomass resources are considered as waste and burnt off in the field after the harvest of cotton crop. Based on recent research, the biomass from cotton crops after the fiber is extracted can be utilized as industrial raw material, source of bioenergy, animal feed, and amendment to the soil. (Pandirwar et al., 2023a ). Cotton stalks have a calorific value ranging from 16.4 to 18.26 MJ/kg of dry matter. (Al Afif et al., 2019 ; Pandirwar et al., 2023b ). Unlike other agricultural crop residues, the fiber from cotton stalks is comparable to that of the most widely available species of wood. Therefore, it is better suitable for a range of industrial applications, including the production of particleboard, hardboard, corrugated boxes, paper and pulp, bioenergy and power plant fuel (Silverstein et al., 2007 ). Cotton stalks are an excellent material to raise edible oyster mushrooms due to their lignocellulosic nature (Sutaria et al., 2016 ). Among these several applications, the only one widely adopted potential uses of cotton stalks in present scenario is that of fuel. This is mainly due to the unavailability of mechanized facilities required to uproot the stalks along with roots and transfer the stalks from the field to locations where they might be put for other uses. However, predominantly followed manual uprooting of the deep-rooted cotton stalk by local practices is a costly as well as drudgerous operation and has lower stalk uprooting efficiency with high labour requirement. In another method, mobile cotton stalk shredders cut and shred the plant stems above the ground while leaving the roots beneath soil. Full-size cotton roots do not decay before succeeding planting season which eventually creates disruptions during tillage operations in subsequent season. Cotton crop residues are often ploughed or incinerated into the soil but it may host insects that can invade the subsequent cotton crop (Huang et al., 2012 ). Consequently, the complete pulling of cotton stalks along with the roots is economically and environmentally most feasible method for comprehensive disposal of cotton residues and its use as a raw material. Few investigations have been carried out worldwide in past on cotton stalk pullers such as 2-row pull type implement called bobby machine (Pothecarey and Field, 1968), counter-rotating wheel type stalk puller (Sumner et al., 1984a ; Sumner et al., 1984b ). In recent years some studies have also been conducted specially for uprooting and shredding of deep-rooted cotton crop residues. Khan et al. ( 2023 ) reported the cotton stalk puller cum shredder that perform integrated operations such as cutting crop leftovers, mixing plant waste with soil and sowing subsequent crop in single run by conserving input resources. However, no commercially available technology exists for complete uprooting of the cotton stalks along with roots after cotton harvesting. Therefore, in areas where cotton is grown, equipment must be available to harvest and collect the cotton residue to use it as a suitable fuel (Sumner et al., 1984b ). Thus, uprooting the cotton residue after cotton harvesting and supply it as a raw material to the biomass-based industries would be a feasible option to overcome the problem of residue management. Therefore, long-term ultimate aim is to develop an uprooting mechanism that can be integrated with the available commercial mobile stalk shredders, so, that an integrated machine can uproot the stalks and simultaneously shred it on the go for its direct use as a raw material. Operation optimization is a statistical procedure which involves combination of several variables with a purpose of finding finest output. This technique could also be applied in maximising the desired outcome of the machine for example the uprooting efficiency in case of cotton stalk puller. Response surface methodology (RSM) is an advanced mathematical and statistical tool used to evaluate the relationship between multiple independent input variables and output response variables. It optimizes these independent and response variables to get the best responses (Taoufik et al., 2022 ). Optimization of most of the agricultural machines is often achieved by application of Response surface methodology (RSM). Cai et al. ( 2024 ) optimized missing pulling rate and breakage rate of a wheel-belt type cotton stalk puller using a multiple quadratic regression response surface model and found optimal values of operating parameters such as cotton stalk pulling angle, tractor forward speed and clamping speed pulling component. However, RSM has limitations in the range of independent input variables due to its non-linear nature (Raj et al., 2021 ). On the contrary, the artificial neural network (ANN) is an excellent and highly robust modelling tool commonly used in complex and nonlinear processes, which can effectively overcome the limitations of RSM (Tao et al., 2014 ). Numerous researchers have also investigated the use of artificial neural networks (ANNs) to predict the performance metrics of agricultural machineries. ANNs excel at capturing complex non-linear relationships between input and output data, making them invaluable for modelling various agricultural equipment (Anantachar et al., 2010 ; Pareek et al., 2021 ). Recently, metaheuristic search algorithms, particularly evolutionary algorithms (EAs) such as genetic algorithms and differential evolution are favoured for their faster convergence rates and cost-effectiveness in optimization tasks. In particular, Particle Swarm Optimization (PSO) has gained attention for its efficacy in modelling the operations of various agricultural machinery (Pareek et al., 2023 ). PSO has been found to outperform traditional statistical techniques in modelling precision (Kumar et al., 2009 ; Anantachar et al., 2010 ). Researchers have employed a range of optimization techniques aimed at identifying the optimal configurations of operating parameters to enhance the efficiency of agricultural machinery operations. By leveraging these advanced methods, significant improvements in the performance and reliability of agricultural equipment can be achieved. At present, RSM and ANN have been widely used in structural design and operation optimization of agricultural machinery (Anantachar et al., 2010 ; Pareek et al., 2021 ; Pareek et al., 2023 ). The integration of these optimization techniques with ANN models represents a promising avenue for further advancements in agricultural machinery technology. Xue et al. (2021) optimized the performance of green forage maize harvester header using a combined Response Surface Methodology (RSM) - Artificial Neural Network (ANN) Approach. Therefore, Response Surface Method (RSM) and combined Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) approach is proposed in the study for optimizing, modelling and predicting the performance parameters of the cotton stalk puller. Consequently, the aim of this study was to develop a device that could completely uproot cotton stalks and other alike deep-rooted crops. However, it was necessary to optimise the performance of the developed cotton stalk puller to get maximum uprooting efficiency and to refine the development in future. Therefore, a study was undertaken to find the optimal combination of the operational (counter-rotating drum speed, forward speed) and design parameter (drum inclination) which directly affect the performance of machine in the field. Materials and methods Design of cotton stalk puller The cotton stalk puller machine was designed such that it can perform the intended task of uprooting the deep-rooted crops stalks of diameter upto 25 mm in vertisol which is the hardest soil. The machine was developed considering all type of planting geometry and plant sizes available in conventional planting as well as high density planting system (HDPS). A cotton stalk pulling unit consist of a pair of 400 mm long counter rotating tapered drums with 190 mm larger and 160 mm smaller section diameter, respectively (Fig. 1 ). Drums are mounted parallelly on a rigid frame. Both, drums are covered with 4, 8 and 12 mm thick rubber sheets of lengths 90, 163 and 147 mm respectively, so as to form variable drum clearance to produce three stage uprooting effect (Fig. 2). Three stage variable drum clearance arrangement provides enough opportunity to the standing stalks of different diameters to get uprooted in any of the three stages thus increases overall uprooting efficiency of the machine. Out of two tapered drums, one is fixed on frame while larger section of other drum is movable and consist of spring loaded drum clearence adjusting mechanism (Fig. 2 and Fig. 3). A spur gear with outer diameter of 210 mm and 28 number of teeths is fixed at larger end of both the drums such that both gears are in mesh with each other (Fig. 1 and Fig. 2). The larger end of one of the two counter-rotating drums is provided with spring loaded drum clearence adjusting mechanism (Fig. 2 and Fig. 3). Spring loaded drum clearence adjustment mechanism helps counter-rotating drums to adjust the clearence according to the thickness or size of entering cotton stalks thus extend the utility of machine for wide range of stalks sizes. A provision was also provided in drum clearence adjustment mechanism to set the machine for specific sizes of cotton stalks. The pressure with which counter-rotating drums are pushed against each other and drum clearence in running condition both are adjusted by adjusting the exposed length of compression spring with the help of pair of studs. When stalks of larger diameter enters the machine, the movable drum moves in outward direction while the spur gears still meshed (Fig. 4 ). The maximum distance with which movable drum moves sideward is equal to the whole depth of gear. The compression spring present in drum clearence adjusting mechanism again press the movable drum to original position after uprooting of stalk. However, the machine was designed to uproot the stalks having maximum diameter of 25 mm. A side cover was also provided as a protective shield around the outer exposed portion of rotating drums (Fig. 3). It is designed such that, it shields overall exposed drum length, drum surface and rotating spur gear. A pair of crop row dividers are provided at the front side of the cotton stalk pulling unit (Fig. 3). The row dividers are positioned such that, it guides the standing crop and gathers widely spread branches into the clearence between rotating drums through stalk guide. A pair of crop stalk guide are provided in between crop row dividers and rotating drums to guide the gathered stalks from row dividers into variable clearence between counter rotating tapered drums (Fig. 3). The spring loaded stalk guides can adjust the opening as per the size of entering stalk material. The desired output of the machine at the end should be complete uprooting with the intact roots of the standing stalks with minimum breakage and miss as shown in Fig. 5 . Experimental plan and statistical analysis The machine was tested for three independent variables namely forward speed of operation (3 levels; 1.37, 1.67 and 1.95 km/h), counter-rotating drum speed (4 levels; 250, 300, 350 and 400 rpm) and counter-rotating drum inclination (3 levels; 0 0 , 10 0 , 20 0 ) for 30 m length of run as shown in Table 1 . The dependent variables such as total number of plants in a selected row, number of plants uprooted, number of plants broken, and number of plants left were recorded. The performance parameters such as uprooting effciency, percent plant broken and percent plant left were also computed (Table 1 ). Table 1 Experimental variables for optimization study of cotton stalk puller Independent variables SN Variables Levels 1 2 3 4 (i) Forward speed, km/h 3 1.37 1.67 1.95 - (ii) Drum speed, rpm 4 250 300 350 400 (iii) Drum inclination, deg 3 0 10 20 - Dependent variables Uprooting efficiency (%); Plant broken (%); Plant left (%) The analysis of the experimental data was done using statistical package SAS 9.3 at 5% level of significance by Tukey’s honest significant difference (HSD) test which is a statistical test and single-step multiple comparison procedure. The test compares the difference between each pair of means with suitable adjustment for the multiple testing. Simple two-way analysis of variance (ANOVA) was also done for dependent variables and p-value was used to analyse the effect of independent variables. Response surface methodology (RSM) using full factorial design data was used to optimise cotton stalk uprooting operation by the developed cotton stalk puller machine. The effect of counter-rotating drum speed, drum inclination and forward speed on uprooting efficiency and plants broken were investigated to enhance the performance of the machine on field. Prediction model using Hybrid PSO- ANN Approach An Artificial Neural Network (ANN) with multilayer feed forward back propagation architecture was developed for the counter-rotating cotton stalk puller in MATLAB 2019b. The network included tan-sigmoid activation for hidden layers and linear functions for output layers. The Levenberg-Marquardt algorithm facilitated model learning. The architecture featured seven input neurons, six output neurons, and a hidden layer configured with 7 and 13 neurons, resulting in a 3-7-13-3 structure. Training used 36 data sets, with 90% for training and 10% for testing. The ANN achieved an R² value of 0.942, indicating strong prediction accuracy. Performance evaluation using R², RMSE, and RPD validated the model's reliability and precision, with an R² of 0.942 showing a strong correlation between predicted and actual values and a low RMSE indicating minimal prediction errors as per Eqs. 1 – 3 . $$\:{R}^{2}=1-\frac{\sum\:_{i=1}^{N}{\left({y}_{ai}-{y}_{pi}\right)}^{2}}{\sum\:_{i=1}^{N}{\left({y}_{ai}-\stackrel{-}{{y}_{a}}\right)}^{2}}$$ 1 $$\:RMSE\:=\sqrt{\frac{\sum\:_{i=1}^{N}{\left({y}_{ai}-{y}_{pi}\right)}^{2}}{N}}$$ 2 $$\:RPD\:=\frac{100}{N}\sum\:_{i=1}^{N}\frac{\left|({y}_{ai}-{y}_{pi}\right|}{\left|{y}_{ai}\right|}\:$$ 3 Where, N represent the number of datasets, \(\:{y}_{ai}\) and \(\:{y}_{pi}\) denote the actual and predicted output values of the i th set, respectively, and \(\:\stackrel{-}{{y}_{ai}}\) represents the mean of actual output values. Optimization using hybrid ANN and PSO Hybrid Artificial Neural Network (ANN) combined with a Particle Swarm Optimization (PSO) algorithm was developed in MATLAB 2019b to optimize the operational parameters of cotton stalk puller using the process shown in Fig. 6 . The initial phase involved training a 3-7-13-3 feed-forward backpropagation ANN with experimental data. After testing for reliability, the ANN mapped the operational parameters of the cotton stalk puller. In the second phase, these parameters were refined using an enhanced PSO algorithm. Two PSO variations were used namely, standard PSO with a constant inertia weight (ω) and Improved PSO with a linearly decreasing inertia weight and confined search space for better optimization and convergence. The study focused on inertia weight for PSO parameter selection. The hybrid ANN-PSO technique integrated the Improved PSO algorithm, with the ANN to enhance optimization outcomes (Sankar et al., 2014 ). The fitness function for the PSO was defined by the error sum-of-squares between required and ANN-predicted parameters, guiding the PSO to minimize this error and optimize the cotton stalk puller's performance as shown in Eq. 4 . $$\:F=\sum\:_{i=1}^{0}{\left({y}_{ri}-\:{y}_{pi}\right)}^{2}$$ 4 Where, F is fitness function, \(\:{y}_{ri}\) and \(\:{y}_{pi}\) are required and predicted i th output parameter respectively, and there is O number of output parameters as in present study O is 3. In the Improved PSO algorithm, the swarm has 100 particles over 1000 iterations. The cognitive parameters c1 and c2 are set to 2. The inertia weight factor ω decreases linearly from 0.9 to 0.4. Positions of swarm particles are constrained within specified bounds. Performance evaluation The cotton stalk puller was mounted at front of the tractor with the help of independent hitching system. The hitching system could also lift or lower down the machine during transportation and adjust the pulling height during operation in the field. The testing of experimental cotton stalk puller machine was carried out for different treatment combinations of three independent variables namely drum speed (250, 300, 350 and 400 rpm), drum inclination (0 o , 10 o and 20 o ) and forward speed (1.37, 1.67 and 1.95 km/h) as stated in Table 1 . The optimization study was conducted at experimental field of ICAR-Central Institute of Agricultural Engineering, Bhopal, India. The cotton crop was grown at plant spacing of 0.60 x 0.75 m followed by majority of cotton farmers in India. The details of soil and crop condition at the time of experiment is given in Table 2 . Each experimental run was conducted by operating the machine for 30 m length of row at selected treatment combinations and performance indicators such as number of plants uprooted, plants broken and plants left were recorded (Fig. 7 ). Table 2 Crop and soil conditions SN Parameter Values Crop conditions 1 Days after harvest, days 8 2 Crop geometry, m 0.6 x 0.75 Stubble dimensions 3 Stubble height, m 1.07 ± 0.23 4 Stem diameter, mm 11.41 ± 2.01 5 Root length, mm 247.8 ± 87.3 Soil conditions 6 Soil type Black cotton 7 Moisture content, % 6.19 ± 1.33 8 Cone index, kPa 2680 ± 927 Uprooting efficiency is an important indicator showing the intended performance of machine. During each test run for selected treatment combination, total number of plants were counted before operating a machine on selected row length of 30 m and number of completely uprooted plants were recorded. Uprooting efficiency is calculated as the number of completely uprooted plants to the total number of plants present in the sample area using Eq. (5) (Solanki and Yadav, 2009 ). Uprooting efficiency (%) = \(\:\:\:\:\:\frac{Number\:of\:completely\:uprooted\:plants}{Total\:number\:of\:plants\:}\) x 100% (5) Out of the total number of plants selected in each experimental run, some stalks could not be uprooted and got broken due to crushing in counter-rotating drums or in set of gears. Number of such broken plants were recorded and shown as percent plant broken using Eq. (6). Plants broken (%) = \(\:\:\:\:\:\frac{Number\:of\:broken\:plants\:}{Total\:number\:of\:plants\:}\) x 100% (6) Besides uprooted and broken plants, some plants among the selected plants could not enter into the machine due to its improper orientation. Number of such un-uprooted or left plants were recorded and shown as percent plant left using Eq. (7). Plants left (%) = \(\:\:\:\:\:\frac{Number\:of\:plants\:left}{Total\:number\:of\:plants}\) x 100% (7) There was no benchmark for ideal uprooting efficiency of any stalk uprooting device as any such technology is not commercially available. For this study, the target for uprooting efficiency of the developed machine was set to be at least 90% to ensure maximum stalk to be removed from the field. Meanwhile, the broken plants should not be more than 10% of the total plants. However, the desired output from the machine should be complete uprooting of the stalks along with the entire root system without plant breakage and minimum plant left (Figs. 8 and 9). Results and discussion Performance evaluation of cotton stalk puller machine Performance result of cotton stalk puller machine for selected independent variables is given in Table 3 in the form of mean values of uprooting efficiency, percent plant broken, and percent plant left. Table 3 also provides a pairwise comparison of these dependent performance variables. Table 3 Performance of cotton stalk puller at different variables Factors Uprooting efficiency,(%) Plant Broken,(%) Plant left,(%) N1 (250) 89.96 A 7.85 B 2.15 A N2 (300) 90.38 A 7.72 B 2.67 A N3 (350) 91.90 A 7.33 B 1.45 A N4 (400) 86.21 B 11.84 A 1.94 A LSD 1.57 2.36 1.63 I1 (0 0 ) 89.42 AB 9.56 A 1.24 B I2 (10 0 ) 90.86 A 7.28 A 1.90 AB I3 (20 0 ) 88.55 B 9.20 A 3.01 A LSD 1.36 2.04 1.41 S1 (1.37) 93.62 A 5.40 C 1.32 A S2 (1.67) 90.13 B 8.06 B 2.00 A S3 (1.95) 85.09 C 12.59 A 2.84 A LSD 1.36 2.04 1.41 Note : N (RPM), I(degree), S(km/h) # Data are mean values. Different letters indicate significant difference (P < 0.001) by Tukey’s test within the same row The mean uprooting efficiency was maximum i.e. 91.90% at drum speed of 350 rpm while it was minimum (86.21%) at 400 rpm drum speed (Table 3 ). Because at heigher drum speed number of broken plants were increased. At 10 0 drum inclination, about 90.86% plants were uprooted, which was highest as compared to 0 0 (89.42%) and 20 0 (88.55%). Uprooting efficiency was also influenced by forward speed of operation i.e. with the increase in travel speed uprooting efficiency decreased. It was maximum (93.62%) at speed 1.37 km/h as compared to 90.13% and 85.09% at 1.67 km/h and 1.95 km/h speed, respectively (Table 3 ). Uprooting efficiency were significantly affected by drum speed and forward speed of travel at 1% level of significance while it was affected significantly at 5% level of significance by drum inclination (Table 4 ). A two-row cotton stalk pulling machine developed by Yumak and Evcim ( 1990 ) also had an uprooting efficiency of 95% in good conditions. Plant broken was lowest (7.33%) at drum speed of 350 rpm whereas it was maximum i.e. 11.84% at 400 rpm drum speed (Table 3 ). Plants broken was significantly affected by drum speed at 1% level of significance (Table 4 ). About 7.28% plants were broken at drum inclination of 10 0 which was minimum as compared to 9.56% and 9.20%, respectively at 0 0 and 20 0 . The forward speed of travel also significantly affected plants broken at 1% level of significance (Table 4 ), which was increased with the increase in forward speed. Plant broken was significaltly lower (5.40%) at 1.37 km/h forward speed as compared to 8.06% and 12.59%, respectively at 1.67 km/h and 1.95 km/h forward speed (Table 3 ). Yumak and Evcim ( 1990 ) reported 2% and 6% broken stalks and stalks which were not pulled, respectively, in two row cotton stalk pulling machine. Plants left are nothing but the plants which were not able to pass through the crop row divider and crop guide to the clearence between two counterrotating drums. It is minimum at drum speed 350 rpm, at drum inclination 0 0 and forward speed of 1.37 km/h (Table 3 ). However, plants left is mainly dependent upon the crop orientation from vertical and skill of the operator using the machine. Tractor driver could not follow the standing cotton crops at heigher forward speed which leads to increase in plants left to 2.84% at 1.95 km/h forward speed. The percent plants left was independent i.e. was not significantly affected by drum speed, drum inclination and forward speed of travel (Table 4 ). Table 4 p-values from ANOVA of uprooting efficiency, plants broken and plants left Source DF p-value Uprooting Efficiency Plants broken Plants left RPM 3 < .0001** 0.0012* 0.5099 Inclination 2 0.0055* 0.0640 0.0490* RPM*Inclination 6 0.9924 0.9272 0.6154 Speed 2 < .0001** < .0001** 0.1060 RPM*Speed 6 0.3857 0.4562 0.9681 Inclination*Speed 4 0.9414 0.9613 0.8101 RPM*Inclination*Speed 12 0.9052 0.5255 0.1719 (Note: **Significant at 1%; *Significant at 5%) DF : Degree of freedom Optimization of performance parameters using RSM Response Surface Methodology (RSM) using full factorial design data was used to optimise the plants uprooted to be maximum and plants broken to be mimimum by the cotton stalk puller machine. The effect of counter-rotating drum speed, drum inclination and forward speed of operation on performance parameters such as uprooting efficiency, plant broken and plants left were investigated in order to enhance the uprooting efficiency of the machine. Optimization of stalk uprooting efficiency The optimization of uprooting efficiency by the cotton stalk puller showed the optimum values of 332.512 rpm, 8.362° and 1.37 km/h for counter-rotating drum speed, drum inclination and forward speed of operation, respectively. However, the response variable uprooting efficiency has an optimal value of 96.59% with individual and combined desirability of 0.97 and 0.88 which shows the closeness of the predicted values to the observed values. Analysis of variance for regression model obtained for optimization of uprooting efficiency of cotton stalk puller is shown in Table 5 . The significance of model is indicated by its F-value of 24.54. The probability of an F-value this large occurring due to noise is merely 0.01%. The model established is significant as characterized by F-value which indicates that at least one of the independent variables contributed to response seen in uprooting efficiency. Uprooting efficiency was significantly affected by drum speed and forward speed as shown in Table 5 . It demonstrates that the effect of distinction of independent parameters on the uprooting efficiency is not by chance. Regression model for relationship between uprooting efficiency and selected independent variables is presented in Eq. (8). UE = 93.43–1.46* DS − 0.43* I − 4 .26* FS -0.0106* (DS) * (I) + 0.11* (DS) * (FS) -0.12* (I) * (FS) -3.44* DS 2 -1.87* I 2 - 0.9153* FS 2 (R 2 = 0.78) (8) Where, UE = Uprooting efficiency, DS = Drum speed, I = Drum Inclination, FS = Forward Speed The co-efficient of determination (R 2 ) was found to be 0.78. When developing a statistical model for optimization, an R 2 value larger than 0.75 is appropriate. (Peng et al., 2019 ; Wondi et al., 2024 ). The findings suggest that 78% of the overall variability in the uprooting efficiency of cotton stalk puller can be attributed to variations in the independent variables. However, the regression model in Eq. 8 was found to be adequate, as evidenced by the insignificant lack of fit. Some plants with more stem and root diameter than designed value was crushed and broke, which contributed to the lower uprooting efficiency. The interactions between independent variables and uprooting efficiency are shown in Figs. 10 – 12 . Uprooting efficiency to increase as the drum speed increases up to 310 rpm and then later on it starts decreasing with the increase in drum speed beyond 310 rpm as revealed in Fig. 10 – 11 . This is because at higher drum speed some thinner stalks starts breaking due the friction from harder rubber coatings and application of more power than required. However, the uprooting efficiency decreased with increase in forward speed of operation as shown in Fig. 11 – 12 . With the increase in forward speed, the plant could not enter upright in the clearance between rotating drums and starts breaking without getting uprooted. Counter-rotating drums also do not get enough time to hold and uproot the plants which increased the plants left. The maximum uprooting efficiency obtained was around the 10 0 of drum inclination as shown in Fig. 10 and Fig. 12 . At perfect horizontal orientation of the drums i.e. at 0 0 , plant cannot stand upright and tends to inclined towards direction of travel due to minimum drum-stem contact which results in more breakage of the stem. However; there is more drum-stem contact area at 20 0 inclination due to which the uprooted or broken stalks could not release properly in upright orientation. Table 5 ANOVA for model regression for uprooting efficiency Source Sum of Squares DF Mean Square F-value p-value Model 1199.97 9 133.33 24.54 < 0.0001 ** DS 84.97 1 84.97 15.64 0.0002 ** I 8.92 1 8.92 1.64 0.2049 * FS 871.80 1 871.80 160.46 < 0.0001 ** DS*I 0.0030 1 0.0030 0.0006 0.9813 * DS*FS 0.3234 1 0.3234 0.0595 0.8081 * I*FS 0.4673 1 0.4673 0.0860 0.7703 * DS² 168.01 1 168.01 30.92 < 0.0001 ** I² 56.12 1 56.12 10.33 0.0021 ** FS² 13.37 1 13.37 2.46 0.1218 * Residual 336.85 62 5.43 Lack of Fit 136.77 26 5.26 0.9465 0.5517 * Pure Error 200.08 36 5.56 Correlation Total 1536.81 71 **Significant at 5% level; *Not significant at 5% level ; DF : Degree of freedom Optimization of plant broken The optimum values of 317.5 rpm, 9.33 o , and 1.37 km/h were obtained for drum speed, drum inclination and forward speed of operation while the response variable plants broken was found to be minimum i.e. 2.38%, at a desirability of 0.85 which indicates the nearness of the predicted values to the observed values. The results of the analysis of variance as shown in Table 6 , which proved the statistical significance of the regression model relating the plants broken to the drum speed, drum inclination and forward speed of operation at 95% confidence level (p < 0.05). The F-value 9.41 of model suggests that it is significant. The chance of an F-value this large occurring due to noise is merely 0.01%. As indicated by the F-value, the developed model is significant, suggesting that at least one of the independent variables contributed to the response seen in broken plants. Plants broken were significantly influenced by drum speed and forward speed of operation as given in Table 6 . The regression model for the relationship between plants broken and the independent variables is presented in Eq. (9). The coefficient of determination (R 2 ) was found to be 0.58. This indicates that 58% of the total variability in the plants broken by the cotton stalk pulling machine can be attributed to variations in the independent variables. The lack of fit is not statistically significant as demonstrated in Table 6 , implying that the regression model is adequate in Eq. (9). PB = 5.09 + 1.74* DS − 0.1669* I + 3.60* FS + 0.2063* (DS) * (I) - 0.2815* (DS) * (FS) -0.2710* (I)*(FS) + 2.61* DS 2 + 2.10* I 2 + 1.06* FS 2 R 2 = 0.58 (9) Where, PB - Plants broken, DS - Drum speed, rpm; I - Drum inclination, deg; FS - Forward speed, km/h The interactions between plants broken and independent variables are shown in Fig. 13 – 15 . The plants broken increased with increase in both forward speed and drum speed beyond 310 rpm as indicated in Fig. 13 – 14 . Plant broken was minimum at forward speed 1.37 km/h and increased as the speed increased upto 1.95 km/h (Fig. 13 – 14 ). The increase in the plant broken with forward speed could be due to lack of sufficient time available for the stalks to get uprooted due to the counter-rotating action of the drum and contact with the rubber. Due to faster forward motion of the machine, the standing stalk gets broken into the pair of spur gears or gets tilted in the direction of travel and broken from the weaker section of the stem. On the other hand, at very high drum speeds, the stalks with smaller diameters gets rubbed at very high intensity and breaks. Therefore, plant broken increased with increase in drum speed beyond 310 rpm. However, plant broken was minimum around 10 0 of drum inclination and beyond which it increased in both direction towards 0 o and 20 o as shown in Fig. 14 – 15 . Table 6 ANOVA for model regression for plants broken Source Sum of Squares DF Mean Square F-value p-value Model 931.00 9 103.44 9.41 < 0.0001 ** DS 121.10 1 121.10 11.02 0.0015 ** I 1.34 1 1.34 0.1216 0.7285 * FS 621.62 1 621.62 56.54 < 0.0001 ** DS*I 1.14 1 1.14 0.1033 0.7490 * DS*FS 2.11 1 2.11 0.1923 0.6626 * I*FS 2.35 1 2.35 0.2138 0.6454 * DS² 96.89 1 96.89 8.81 0.0042 ** I² 70.84 1 70.84 6.44 0.0137 ** FS² 17.94 1 17.94 1.63 0.2063 * Residual 681.60 62 10.99 Lack of Fit 255.73 26 9.84 0.8315 0.6846 * Pure Error 425.87 36 11.83 Cor Total 1612.60 71 **Significant at 5% level; *Not significant at 5% level DF : Degree of freedom Optimization of percent plant left The counter-rotating drum speed, drum inclination, and forward speed of operation were found to have optimal values of 339.27 rpm, 3.68°, and 1.37 km/h. The response factor, on the other hand, had an optimal value of 0.67% for plant left with a desirability of 0.89, indicating the closeness of the predicted values to the observed values. The findings for the Analysis of variance of regression model relating the plant left to the independent variables are presented in Table 7 . The results from the analysis of variance test indicated that the regression model in Eq. (10) was statistically significant at a 95% confidence level (p < 0.05). The obtained regression model is not significant, as depicted by the model F-value of 4.06. The probability of an F-value this large occurring due to noise is only 1.03%, indicating that the independent variables have the least impact on the observed responses in terms of the percentage of plants left. The regression model for the relationship between plants left and the independent variables is presented in Eq. (10). The coefficient of determination (R 2 ) was found to be 0.15. The variation in the independent variables is demonstrated to explain 15% of the total responses observed in the plants left by the machine. The selected independent variables have least effect on response variable plants left. Though it is selected into the study because it indirectly affects the uprooting efficiency. Lack of fit was also not significant as shown in Table 7 . The low co-efficient of determination indicated the inadequacy of the regression model in Eq. (10) although the lack of fit was insignificant. PL = 2.05–0.2780* DS + 0.8837* I + 0.7599*FS R 2 = 0.15 (10) Where, PL - Plants left, DS - Drum speed, I - Drum Inclination, FS - Forward Speed Figures 16 – 18 show the interactions between plants left and independent variables. The drum speed has the least effect on the plants left while the drum inclination and forward speed significantly affect the plants left. However, the plants left increased with increase in the drum inclination (Fig. 16 and Fig. 18 ) and forward speed of operation (Fig. 17 – 18 ). Moreover, the plant left is purely dependent upon the skill of the driver operating the machine which eventually gets reduced with the enhanced skill of the operator. The vertically oriented plants can easily enter into the variable clearance of the machine. However, not all plants in the field are perfectly oriented vertically which may get skipped from entering into the machine and left due to insufficient time available at higher forward speeds. Table 7 ANOVA for model regression for plants left Source Sum of Squares DF Mean Square F-value p-value Model 68.30 3 22.77 4.06 0.0103 * DS 3.09 1 3.09 0.5509 0.4605 * I 37.48 1 37.48 6.68 0.0119 ** FS 27.73 1 27.73 4.94 0.0296 ** Residual 381.70 68 5.61 Lack of Fit 159.42 32 4.98 0.8069 0.7295 * Pure Error 222.28 36 6.17 Cor Total 450.00 71 **Significant at 5% level; *Not significant at 5% level DF : Degree of freedom Validation of RSM models The study generated two different optimal conditions from RSM model and experimental results for maximum uprooting efficiency and minimum plants broken. When comparing the two, the best scenario to adopt is derived from the RSM model, with the main focus being on enhancing the uprooting efficiency. Because, the uprooting efficiency was the most important among all selected response variable under the study. Therefore, the ultimate aim of the study was to maximize the uprooting efficiency of the machine. The RSM model takes into account not only the intended result, uprooting efficiency, but also the critical factor of minimizing plant breakage. The RSM model's conditions are carefully chosen to strike a balance between maximizing uprooting efficiency and minimizing plant broken. Plant broken can have major agronomic and operational implications. This choice is based on a comprehensive strategy, recognizing that finding the perfect equilibrium among various elements, such as efficiency, can lead to better outcomes overall in the scenario of removing cotton stalks from the field. The models obtained for uprooting efficiency, plants broken and plants left by cotton stalk puller machine were validated by comparing experimental values with the predicted test values. The coefficient of determination (R 2 ) for uprooting efficiency, plants broken and plants left were 0.78, 0.58 and 0.15, respectively. Furthermore, there was a strong correlation between the estimated and experimental data. A normal probability of residuals was also observed to understand the nature of fitting. The fitting of regression data indicated that it is nearby a straight line, representing that error terms are normally distributed. Consequently, the hypothesis of analysis is met. The second order polynomial model fits the relationship between uprooting efficiency and plants broken, based on the F-value and R 2 . The plots between the predicted and observed values revealed high degree of agreement between the predicted and observed values for uprooting efficiency and plants broken. This showed a strong correlation between the experimental and predicted values of plants broken and uprooting efficiency as shown in Figs. 19–20. It demonstrated how well the data fit the model and provided a strong estimate of the response for the uprooting efficiency and plants broken in the range of independent variables under study. However, the correlation between the observed and predicted values of plants left were low as shown in Fig. 21 . The experimental values for uprooting efficiency, plants broken, plants left were 96.59, 2.38 and 0.67% respectively within the range of 1.37–1.95 km/h for forward speed, 250–400 rpm for counter-rotating drum speed and 0–20° for drum inclination under optimal conditions of 1.37 km/h, 332.512 rpm and 8.362° for uprooting efficiency; 1.37 km/h, 317.54 rpm and 9.33° for plants broken and 1.37 km/h, 339.27 rpm and 3.68° for plants left for forward speed, counter-rotating drum speed and drum inclination, respectively. The deviations between predicted and observed values of uprooting efficiency, ranged from 0.05–3.03, 0.07–2.88 and 0.12–1.80 for uprooting efficiency, plants broken and plants left respectively. The adequacy of models established in describing the observed data and closeness of the observed values to the predicted values were also demonstrated by the desirability values of 0.97, 0.85, and 0.89 of various response variables as shown in Fig. 22 . The models obtained for the cotton stalk uprooting operation adequately described the observations. Optimization using Hybrid PSO-ANN algorithm Optimization of cotton stalk puller performance parameters was achieved by integrating the top-performing prediction model with Particle Swarm Optimizer (PSO). The PSO effectively explored performance parameters using an inertia weight of 0.9, acceleration coefficients of 2, a swarm size of 100, and 1000 iterations. The convergence curve revealed that the mean fitness values approximated the global best for uprooting efficiency, plant broken, and plant left, by iterations 985, 989, and 997, respectively, which indicates convergence towards optimal solutions (Fig. 19). Table 3 shows the optimal parameters from the ANN-PSO model, with forward speed at 1.37 km/h, drum inclination at 7.89 degrees, and drum speed at 331.45. This integration of prediction models with optimization algorithms exemplifies a sophisticated approach to enhancing cotton stalk puller performance. The convergence curve analysis highlighted the gradual improvement in fitness values, with mean and global best fitness values closely aligning, demonstrating the PSO's effectiveness. The optimized parameters maximize plant uprooting efficiency while minimizing plant broken and plant left showcasing a systematic method for fine-tuning mechanical systems in agricultural applications. Table 8 also shows the deviation or percentage error of 1.94% among observed and predicted values of uprooting efficiency. The results are reliable and adequate when percentage error is below 15% (Wondi et al., 2024 ). Table 8 Optimized performance parameters by ANN-PSO Forward Speed, (km/h) Inclination Angle, (deg) Drum speed, (rpm) Plant Uprooting efficiency Predicted Observed Deviation (%) 1.37 7.89 331.45 94.84 96.72 1.94 Conclusions The optimization of a counter-rotating cotton stalk pulling machine revealed the optimum values for operational and design parameters such as forward speed, counter-rotating drum speed and counter-rotating drum inclination. The performance of machine such as uprooting efficiency, plants broken and plants left were significantly affected by the selected parameters based on the findings of the study. The optimal values of 332.5 rpm, 8.36 degrees and 1.37 km/h were obtained for drum speed, drum inclination and forward speed, respectively for the selected performance parameters using RSM. However, the plants uprooted, plants broken and plants left showed the optimum values of 96.59%, 2.81% and 1.11%, respectively. The uprooting efficiency and plants broken were highly significantly affected by drum speed and forward speed of travel. ANN-PSO model provided 1.37 km/h forward speed, 7.89 0 drum inclination and 331.45 rpm counter-rotating drum speed as optimal parameters with only 1.94% error in the observed and predicted values of uprooting efficiency. The developed machine achieved the expected uprooting efficiency of over 90% with less than 10% of plants broken. In short both, the RSM and combined ANN-PSO approach can better predict and optimize cotton stalk puller performance. Both, methods have given nearly same results, therefore, either of the two methods can be used for optimization of machine operation. The models acquired for the stalk uprooting operation were deemed sufficient to explain the results. The findings of study would provide valuable information for operation of developed machine under different operating conditions and other deep-rooted crops as well. It also offers valuable information for integrating the developed machine with available mobile stalk shredders to get complete solution of uprooting and shredding of cotton stalks in one operation. Declarations Acknowledgments Ministry of Agricultural, GOI, and ICAR-Central Institute of Agricultural Engineering, Bhopal supported this research project. The authors also express their gratitude to ICAR-Central Institute for Cotton Research, Nagpur for their technical advisory support. Authors' contributions Ashutosh P Pandirwar: Conceptualization, Writing original draft, Data curation, Formal analysis. Himanshu S Pandey: Writing review, Project administration & editing. Ajit Magar: Conceptualization, Data collection. Ajay K Roul: Supervision, Project administration. Manoj Kumar: Formal analysis, Supervision. Bikram Jyoti: Writing – review & editing, Formal analysis. Funding Not applicable. Data Availability Data will be provided on request Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Declaration of Competing Interest The authors hereby certify that they have no known competing financial interest or personal relationships that could have appeared to influence the research work reported in this publication. References Al Afif R, Pfeifer C, Pröll T. Bioenergy recovery from cotton stalk. In: Mahmood-ur-Rahman Ansari (ed) Advances in Cotton Research. Intech Open. 2019; 1–19. https://doi.org/10.5772/intechopen. 88005 Anantachar M, Kumar PG, Guruswamy T. Neural network prediction of performance parameters of an inclined plate seed metering device and its reverse mapping for the determination of optimum design and operational parameters. Comput. Electron. Agric. 2010; 72(2): 87-98. Available from: https://doi.org/10.1016/j.compag.2010.03.001 Cai J, Zhang J, Gao Z, et al. Design and test of a wheel-belt type cotton stalk puller. Int. J. Agric. Biol. Eng. 2024; 17 (2): 102-108. DOI: 10.25165/j.ijabe.20241702.7287 Directorate of Economics and Statistics (DES), Second Advance Estimates of Production of Foodgrains, Oilseeds and other Commercial Crops for the 2023-24. Department of Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture and Farmers’ Welfare, Government of India, New Delhi. 2024. https://desagri.gov.in/wp-content/uploads/2024/03/Time-Series-Production-2nd-AE-2023-24-English.pdf. Accessed 26 Apr 2024. Fawzy S, Osman AI, Farrell C, et al. Characterization and kinetic modelling for pyrolytic conversion of cotton stalks. Energy Sci. Eng. 2021; 9 (10): 1908-1918. https://doi.org/10.1002/ese3.961 Food and Agricultural Organization STAT (FAOSTAT). Crops and livestock products. 2022. https://www.fao.org/faostat/en/#data/QCL. Accessed 26 Apr 2024. Huang W, Bai Z, Hoefel D, et al. Effects of cotton straw amendment on soil fertility and microbial communities. Front. Environ. Sci. Eng. 2012; 6: 336-349. https://doi.org/10.1007/s11783-011-0337-z ICAC. Cotton this month for the month of April 2023, International Cotton Advisory Committee (ICAC) (WWW document). 2023. URL (https://icac.org/Content/PublicationsPdf%20Files/baa65705_ad54_41d7_bd5d_ad89ec88df6b/CTM_2023_04_03.pdf.pdf) (accessed 13.06.2024) Khan AA, Umair S, Rudra RP, et al. Structural analysis of cotton stalk Puller and Shredder Machine. Alexandria Eng. J. 2023; 64: 335–347 https://doi.org/10.1016/j.aej.2022.09.002 Kumar GP, Srivastava B, Nagesh DS. Modelling and optimization of parameters of flow rate of paddy rice grains through the horizontal rotating cylindrical drum of drum seeder. Comput. Electron. Agric. 2009; 65(1): 26-35. Available from: https://doi.org/10.1016/j.compag.2008.07.006 Meng Q, Gu J, Xu Z, et al. Comparative analysis of genome sequences of the two cultivated tetraploid cottons, Gossypium hirsutum (L.) and G. barbadense (L.), Ind. Crops Prod. 2023; 196: 116471. https://doi.org/10.1016/j.indcrop.2023.116471 Pandirwar AP, Khadatkar A, Mehta CR, et al. Technological advancement in harvesting of cotton stalks to establish sustainable raw material supply chain for industrial applications: A review. BioEnergy Res. 2023a; 16 (2): 741-760. https://doi.org/10.1007/s12155-022-10520-3 Pandirwar AP, Pandey HS, Magar AP, et al. Physical, chemical, thermal, and mechanical properties of cotton stalk: An industrial multi-purpose cotton by-product. J. Agric. Eng. 2023b; 60(2): 188-204. DOI: 10.52151/jae2023602.1807 Pareek CM, Tewari VK, Machavaram R. Multi-objective optimization of seeding performance of a pneumatic precision seed metering device using integrated ANN-MOPSO approach. Eng. Appl. Artif. Intell. 2023; 117 : 105559. Available from: https://doi.org/10.1016/j.engappai.2022.105559 Pareek CM, Tewari VK, Machavaram R, et al. Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ANN-PSO approach. Artif. Intell. Agric. 2021; 5 : 1-12. Available from: https://doi.org/10.1016/j.aiia.2020.11.002 Peng WL, Mohd-Nasir H, Setapar SHM, et al. Optimization of process variables using response surface methodology for tocopherol extraction from Roselle seed oil by supercritical carbon dioxide. Ind. Crops Prod. 2019; 143: 111886 https://doi.org/10.1016/j.indcrop.2019.111886. Pothecarey BP, Ofield RJ. Destruction of old cotton for pest and disease control. World Crops. 1968; 20(8): 39-43. Raj JVA, Kumar RP, Vijayakumar B, et al. Modelling and process optimization for biodiesel production from Nannochloropsis salina using artificial neural network. Bioresour. Technol. 2021. 329 : 124872. https://doi.org/10.1016/j.biortech.2021.124872 Ramanjaneyulu AV, Ramprasad B, Sainath N, et al. Crop residue management in cotton. Chron. of Bioresour. Management. 2021; 5 (Mar, 1): 001-008. Sankar PA, Machavaram R, Shankar K. System identification of a composite plate using hybrid response surface methodology and particle swarm optimization in time domain. Measurement. 2014; 55 : 499-511. Available from: https://doi.org/10.1016/j.measurement.2014.05.025 Silverstein RA, Chen Y, Sharma-Shivappa RR, et al. A comparison of chemical pretreatment methods for improving saccharification of cotton stalks. Bioresour. Technol. 2007; 98:3000–3011. https://doi.org/10.1016/j.biortech. 2006.10.022 Solanki HB, Yadav R. Development and performance evaluation of tractor operated plant uprooter for castor crop. Agric. Mech. in Asia, Africa and Latin America, 2009; 40(2): 41-46. Sumner HR, Hewig RE, Monroe GE. Harvesting cotton plat residue for fuel. Trans. ASAE, 1984b; 27: 968-972. Sumner HR, Monroe GE, Hellwig GE. Design elements of a cotton plant puller. Trans. ASAE, 1984a; 27: 366-369. Sutaria GS, Vora VD, Vekaria PD, et al. Technology for rapid composting of cotton stalk. Int J Agric Sci Res, 2016; 6:211–216 Tao Y, Wu D, Zhang QA, et al. Ultrasound-assisted extraction of phenolics from wine lees: Modeling, optimization and stability of extracts during storage. Ultrason. Sonochem. 2014; 21(2): 706-715. DOI: 10.1016/j.ultsonch.2013.09.005 Taoufik N, Boumya W, Elmoubarki R, et al. Experimental design, machine learning approaches for the optimization and modeling of caffeine adsorption. Mater. Today Chem. 2022; 23 : 100732. https://doi.org/10.1016/j.mtchem.2021.100732 The Cotton Corporation of India (CCI), Statistics. 2024. https://cotcorp.org.in/statistics.aspx. Accessed 26 Apr 2024. Wendel JF, Grover CE. Taxonomy and evolution of the cotton genus, Gossypium. Cotton, 2015; 57: 25-44. https://doi.org/10.2134/agronmonogr57.2013.0020 Wondi MH, Haris NIN, Shamsudin R, et al. Development and testing of an oil palm (Elaeis guineensis Jacq.) fruit digester process for kernel free in crude palm oil production. Ind. Crops Prod. 2024; 208 : 117755. https://doi.org/10.1016/j.indcrop.2023.117755 Xue Z, Fu J, Fu Q, et al. Modelling and optimizing the performance of green forage maize harvester header using a combined Response Surface Methodology–Artificial Neural Network Approach. Agric. 2023; 13 (10): 1890. https://doi.org/10.3390/agriculture13101890 Yumak H, Evcim Ü. 1990. A two-row cotton stalk pulling machine. International Congress on Mechanization and Energy in Agriculture. Proceedings of a conference held in Adana, Turkey, 1-4 October 1990. 1990; 416-425 ref.13 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4874230","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345459636,"identity":"535f62ba-804f-43c8-aefa-d6a7ab6b6e57","order_by":0,"name":"Ashutosh Pandirwar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFACHgYGyX829fwgdkIBsVos2NISJBtAWgyI1VLBdjjB4ACIQ4wW/v6zxyRu8DDnGZ9fnfjhgQGDPL/YAfxaJG7kpUnOkGArNrvxdrME0GGGM2cnELDmBo+ZtIQBD+O2G2c3gLQkGNwmoEX+/Bkz6T8JEoybZ5zd/IMoLQYHcswkJA4YJG7g791GnC2GN3KMLSQbEowlbvBus0gwkCDsF7nzZwxvSDb8lwMG3eabPyps5PmlCWhBAAmwSglilYMA/wFSVI+CUTAKRsFIAgCVGUNKw5g/7wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4545-7082","institution":"ICAR - Central Institute of Agricultural Engineering: Central Institute of Agricultural Engineering","correspondingAuthor":true,"prefix":"","firstName":"Ashutosh","middleName":"","lastName":"Pandirwar","suffix":""},{"id":345459637,"identity":"e0cc8347-28a6-47ba-a65f-338ba75b3990","order_by":1,"name":"HIMANSHU Pandey","email":"","orcid":"","institution":"ICAR - Central Institute of Agricultural Engineering: Central Institute of Agricultural Engineering","correspondingAuthor":false,"prefix":"","firstName":"HIMANSHU","middleName":"","lastName":"Pandey","suffix":""},{"id":345459638,"identity":"f364d372-c334-4e10-8bbe-83d886f2a1dd","order_by":2,"name":"AJIT P Magar","email":"","orcid":"","institution":"Central Institute of Agricultural Engineering","correspondingAuthor":false,"prefix":"","firstName":"AJIT","middleName":"P","lastName":"Magar","suffix":""},{"id":345459639,"identity":"e47b6a8d-2a9d-4fe5-b2f7-ea4a80808bff","order_by":3,"name":"AJAY K Roul","email":"","orcid":"","institution":"ICAR - Central Institute of Agricultural Engineering: Central Institute of Agricultural Engineering","correspondingAuthor":false,"prefix":"","firstName":"AJAY","middleName":"K","lastName":"Roul","suffix":""},{"id":345459640,"identity":"b05acd69-731b-40fe-a99f-941543bf8da2","order_by":4,"name":"MANOJ Kumar","email":"","orcid":"","institution":"ICAR - Central Institute of Agricultural Engineering: Central Institute of Agricultural Engineering","correspondingAuthor":false,"prefix":"","firstName":"MANOJ","middleName":"","lastName":"Kumar","suffix":""},{"id":345459641,"identity":"962fdc02-b588-4f88-926f-8ca06452ccd6","order_by":5,"name":"BIKRAM Jyoti","email":"","orcid":"","institution":"ICAR - Central Institute of Agricultural Engineering: Central Institute of Agricultural Engineering","correspondingAuthor":false,"prefix":"","firstName":"BIKRAM","middleName":"","lastName":"Jyoti","suffix":""}],"badges":[],"createdAt":"2024-08-07 11:10:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4874230/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4874230/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65282264,"identity":"26cc1bc7-adb8-4bb6-8128-68019bb37116","added_by":"auto","created_at":"2024-09-25 15:03:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":321065,"visible":true,"origin":"","legend":"\u003cp\u003eExploded view of cotton stalk pulling drum\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/8be26c30032e657fbb79d082.png"},{"id":65281950,"identity":"16cb6f49-891b-4c75-860e-ac101230fff7","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54019,"visible":true,"origin":"","legend":"\u003cp\u003eCounter-rotating drums with variable drum clearence\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/60cdcfbb494a3144698b8672.png"},{"id":65282252,"identity":"bc5fcaa7-81b6-411d-802c-7e393ff2ba8b","added_by":"auto","created_at":"2024-09-25 15:03:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41974,"visible":true,"origin":"","legend":"\u003cp\u003eCotton stalk puller\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/42ef4f29c61143d673d4f36e.png"},{"id":65282258,"identity":"c3e49511-c5b3-4c2f-9090-27d18f906f9e","added_by":"auto","created_at":"2024-09-25 15:03:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":372511,"visible":true,"origin":"","legend":"\u003cp\u003eWorking of drum clearence adjusting mechanism\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/d70d4d9ad33c9074ffc88007.png"},{"id":65283520,"identity":"f14059d3-e567-4a57-9b8c-bbc964048dc8","added_by":"auto","created_at":"2024-09-25 15:11:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":241700,"visible":true,"origin":"","legend":"\u003cp\u003eUprooting operation with cotton stalk puller\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/fc97ca3bb128bfc0ccc05ef0.png"},{"id":65282253,"identity":"d7236110-ff4f-4cfd-9568-481cd2530a7a","added_by":"auto","created_at":"2024-09-25 15:03:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":114322,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for optimization using Hybrid ANN-PSO\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/eb3c27f3ddd62f41f4b6f356.png"},{"id":65281956,"identity":"67f19392-f271-42bb-9ebd-f2ad3ae56507","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":459352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eField testing of cotton stalk puller\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/1cc5c50f5e9eedeac0999f6a.png"},{"id":65283517,"identity":"020ff8f8-b0fa-4874-8f48-a5aa71afd8ac","added_by":"auto","created_at":"2024-09-25 15:11:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":262575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStalks uprooted by puller\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/a95a4fa68e1cb922bb501920.png"},{"id":65281952,"identity":"39d2d200-9b22-4675-bf92-3332d9264c3e","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":165583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eField cleared from cotton stalks\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/2426b114be89aa572c43fbe7.png"},{"id":65283523,"identity":"16c6d6a1-4f98-469c-b579-43c1eb047bc2","added_by":"auto","created_at":"2024-09-25 15:11:36","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":281746,"visible":true,"origin":"","legend":"\u003cp\u003eUprooting efficiency against drum inclination and drum speed\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/b7e24ae97d910f393e73b506.png"},{"id":65283697,"identity":"81925350-8e7d-455d-ab50-65da6ae200a2","added_by":"auto","created_at":"2024-09-25 15:19:36","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":262274,"visible":true,"origin":"","legend":"\u003cp\u003eUprooting efficiency against forward speed and drum speed\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/3a51edfa651e8e0dff30a453.png"},{"id":65281966,"identity":"1d70b020-7c87-4619-b94e-ba06f816ff6f","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":251636,"visible":true,"origin":"","legend":"\u003cp\u003eUprooting efficiency against forward speed and drum inclination\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/96645f675e9f9411638fb1b5.png"},{"id":65281969,"identity":"bcb456e5-5f17-46ee-9e99-173d7aa693d0","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":223180,"visible":true,"origin":"","legend":"\u003cp\u003ePlant broken against forward speed and drum speed\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/856c5047bee35c944616c5fd.png"},{"id":65281968,"identity":"5cc9818f-46fe-4f1c-9332-e3b40f3021bf","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":205800,"visible":true,"origin":"","legend":"\u003cp\u003ePlant broken against forward speed and drum inclination\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/4e5283df957b8c7b1ff2c1e3.png"},{"id":65281971,"identity":"1f61e3dd-a9c0-4573-a1be-6df93b9f80c8","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":210032,"visible":true,"origin":"","legend":"\u003cp\u003ePlant broken against Drum inclination and drum speed\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/0b3c244ae822bff4ee2f56f1.png"},{"id":65281961,"identity":"3ea7e01f-b95c-42c9-b871-1cbbd20c38f7","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":193583,"visible":true,"origin":"","legend":"\u003cp\u003ePlant left against drum inclination and drum speed\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/1f85d396ffa6fb0c987543be.png"},{"id":65283699,"identity":"a5b2797f-2c5f-4937-b254-8d3c01327377","added_by":"auto","created_at":"2024-09-25 15:19:36","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":182653,"visible":true,"origin":"","legend":"\u003cp\u003ePlant left against forward speed and drum speed\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/fedd0b31f9e1130dbcd5d39c.png"},{"id":65283521,"identity":"e7042374-193e-4e27-a3ae-1b1ad49c17f9","added_by":"auto","created_at":"2024-09-25 15:11:36","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":184997,"visible":true,"origin":"","legend":"\u003cp\u003ePlant left against forward speed and drum inclination\u003c/p\u003e","description":"","filename":"floatimage18.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/625c432ed26fe8241c030256.png"},{"id":65282263,"identity":"0382956d-77a9-4373-ac53-56e3dc0bfab9","added_by":"auto","created_at":"2024-09-25 15:03:36","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":24398,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted values against observed values of uprooting efficiency\u003c/p\u003e","description":"","filename":"floatimage19.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/28c5eeb7eacb953fc4d69f9e.png"},{"id":65284478,"identity":"79b70b51-40d5-4cd9-ad5f-3920362aad62","added_by":"auto","created_at":"2024-09-25 15:27:36","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":73396,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted values against observed values of plants broken\u003c/p\u003e","description":"","filename":"floatimage20.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/a2ace112b34e490ad5758b00.png"},{"id":65281959,"identity":"4b6f7db6-ec7a-4003-9a8d-30197f6d6525","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":12926,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted values against observed values of plants left\u003c/p\u003e","description":"","filename":"floatimage21.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/54b3c3ca958ee23f101f1cc8.png"},{"id":65282259,"identity":"b9e053cf-565e-45ee-be52-3777a959a008","added_by":"auto","created_at":"2024-09-25 15:03:36","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":56312,"visible":true,"origin":"","legend":"\u003cp\u003eDesirability values of different performance parameters\u003c/p\u003e","description":"","filename":"floatimage22.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/99815288040d86556daf8ced.png"},{"id":65281972,"identity":"6a9d079a-07d1-412e-bb3e-56f0ed9a569a","added_by":"auto","created_at":"2024-09-25 14:55:36","extension":"png","order_by":23,"title":"Figure 23","display":"","copyAsset":false,"role":"figure","size":188168,"visible":true,"origin":"","legend":"\u003cp\u003ePSO convergence characteristics on uprooting efficiency, plant broken and plant left\u003c/p\u003e","description":"","filename":"23.png","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/f9b69b1234961a58995c7820.png"},{"id":66531726,"identity":"85e6d810-03c3-4e03-84ec-e082181c6307","added_by":"auto","created_at":"2024-10-14 06:19:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5592377,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4874230/v1/abed4dd6-c1fb-49bd-87bf-54989cdc6fb8.pdf"}],"financialInterests":"","formattedTitle":"Optimization of uprooting efficiency of counter-rotating cotton stalk puller for on-field operations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCotton (Gossypium L.) is a main fiber and oil seed crop having global importance, also more importantly a topic of significant scientific interest (Wendel and Grover, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cotton is mainly grown for fibre in more than seventy countries of the world of which China, India, USA, Brazil, and Australia are leading one accounting for approximately three-quarters of the cotton production of world during year 2022-23 (ICAC, 2023). Worldwide, cotton is cultivated in an area of 31.43\u0026nbsp;million ha with a production of 25.18\u0026nbsp;million tonnes (148.18\u0026nbsp;million bales) during year 2021\u0026ndash;2022 (FAOSTAT, 2022). India has the largest area of 12.47\u0026nbsp;million ha and highest production of 32.31\u0026nbsp;million bales (170 kg per bale) among the cotton growing countries followed by China, United States and Pakistan (CCI, 2024; DES, 2024).\u003c/p\u003e \u003cp\u003eHigh production of cotton is accompanied by generation of tonnes of cotton residues every year (Pandirwar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). About 23\u0026ndash;30\u0026nbsp;million tonnes of cotton residue is produced in India every year at an average rate of 3 tonnes per hectare of area (Ramanjaneyulu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Global cotton residues availability would be estimated between 90.3 and 129\u0026nbsp;million tonnes annually at a current cotton production rate and is projected to grow consequently (Fawzy et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In most part of the world, this invaluable biomass resources are considered as waste and burnt off in the field after the harvest of cotton crop.\u003c/p\u003e \u003cp\u003eBased on recent research, the biomass from cotton crops after the fiber is extracted can be utilized as industrial raw material, source of bioenergy, animal feed, and amendment to the soil. (Pandirwar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Cotton stalks have a calorific value ranging from 16.4 to 18.26 MJ/kg of dry matter. (Al Afif et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pandirwar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Unlike other agricultural crop residues, the fiber from cotton stalks is comparable to that of the most widely available species of wood. Therefore, it is better suitable for a range of industrial applications, including the production of particleboard, hardboard, corrugated boxes, paper and pulp, bioenergy and power plant fuel (Silverstein et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Cotton stalks are an excellent material to raise edible oyster mushrooms due to their lignocellulosic nature (Sutaria et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Among these several applications, the only one widely adopted potential uses of cotton stalks in present scenario is that of fuel. This is mainly due to the unavailability of mechanized facilities required to uproot the stalks along with roots and transfer the stalks from the field to locations where they might be put for other uses.\u003c/p\u003e \u003cp\u003eHowever, predominantly followed manual uprooting of the deep-rooted cotton stalk by local practices is a costly as well as drudgerous operation and has lower stalk uprooting efficiency with high labour requirement. In another method, mobile cotton stalk shredders cut and shred the plant stems above the ground while leaving the roots beneath soil. Full-size cotton roots do not decay before succeeding planting season which eventually creates disruptions during tillage operations in subsequent season. Cotton crop residues are often ploughed or incinerated into the soil but it may host insects that can invade the subsequent cotton crop (Huang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, the complete pulling of cotton stalks along with the roots is economically and environmentally most feasible method for comprehensive disposal of cotton residues and its use as a raw material. Few investigations have been carried out worldwide in past on cotton stalk pullers such as 2-row pull type implement called bobby machine (Pothecarey and Field, 1968), counter-rotating wheel type stalk puller (Sumner et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1984a\u003c/span\u003e; Sumner et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1984b\u003c/span\u003e). In recent years some studies have also been conducted specially for uprooting and shredding of deep-rooted cotton crop residues. Khan et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported the cotton stalk puller cum shredder that perform integrated operations such as cutting crop leftovers, mixing plant waste with soil and sowing subsequent crop in single run by conserving input resources. However, no commercially available technology exists for complete uprooting of the cotton stalks along with roots after cotton harvesting. Therefore, in areas where cotton is grown, equipment must be available to harvest and collect the cotton residue to use it as a suitable fuel (Sumner et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1984b\u003c/span\u003e). Thus, uprooting the cotton residue after cotton harvesting and supply it as a raw material to the biomass-based industries would be a feasible option to overcome the problem of residue management. Therefore, long-term ultimate aim is to develop an uprooting mechanism that can be integrated with the available commercial mobile stalk shredders, so, that an integrated machine can uproot the stalks and simultaneously shred it on the go for its direct use as a raw material.\u003c/p\u003e \u003cp\u003eOperation optimization is a statistical procedure which involves combination of several variables with a purpose of finding finest output. This technique could also be applied in maximising the desired outcome of the machine for example the uprooting efficiency in case of cotton stalk puller. Response surface methodology (RSM) is an advanced mathematical and statistical tool used to evaluate the relationship between multiple independent input variables and output response variables. It optimizes these independent and response variables to get the best responses (Taoufik et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Optimization of most of the agricultural machines is often achieved by application of Response surface methodology (RSM). Cai et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) optimized missing pulling rate and breakage rate of a wheel-belt type cotton stalk puller using a multiple quadratic regression response surface model and found optimal values of operating parameters such as cotton stalk pulling angle, tractor forward speed and clamping speed pulling component. However, RSM has limitations in the range of independent input variables due to its non-linear nature (Raj et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the contrary, the artificial neural network (ANN) is an excellent and highly robust modelling tool commonly used in complex and nonlinear processes, which can effectively overcome the limitations of RSM (Tao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous researchers have also investigated the use of artificial neural networks (ANNs) to predict the performance metrics of agricultural machineries. ANNs excel at capturing complex non-linear relationships between input and output data, making them invaluable for modelling various agricultural equipment (Anantachar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pareek et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, metaheuristic search algorithms, particularly evolutionary algorithms (EAs) such as genetic algorithms and differential evolution are favoured for their faster convergence rates and cost-effectiveness in optimization tasks. In particular, Particle Swarm Optimization (PSO) has gained attention for its efficacy in modelling the operations of various agricultural machinery (Pareek et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). PSO has been found to outperform traditional statistical techniques in modelling precision (Kumar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Anantachar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Researchers have employed a range of optimization techniques aimed at identifying the optimal configurations of operating parameters to enhance the efficiency of agricultural machinery operations. By leveraging these advanced methods, significant improvements in the performance and reliability of agricultural equipment can be achieved. At present, RSM and ANN have been widely used in structural design and operation optimization of agricultural machinery (Anantachar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pareek et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pareek et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The integration of these optimization techniques with ANN models represents a promising avenue for further advancements in agricultural machinery technology. Xue et al. (2021) optimized the performance of green forage maize harvester header using a combined Response Surface Methodology (RSM) - Artificial Neural Network (ANN) Approach. Therefore, Response Surface Method (RSM) and combined Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) approach is proposed in the study for optimizing, modelling and predicting the performance parameters of the cotton stalk puller.\u003c/p\u003e \u003cp\u003eConsequently, the aim of this study was to develop a device that could completely uproot cotton stalks and other alike deep-rooted crops. However, it was necessary to optimise the performance of the developed cotton stalk puller to get maximum uprooting efficiency and to refine the development in future. Therefore, a study was undertaken to find the optimal combination of the operational (counter-rotating drum speed, forward speed) and design parameter (drum inclination) which directly affect the performance of machine in the field.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eDesign of cotton stalk puller\u003c/h2\u003e\n \u003cp\u003eThe cotton stalk puller machine was designed such that it can perform the intended task of uprooting the deep-rooted crops stalks of diameter upto 25 mm in vertisol which is the hardest soil. The machine was developed considering all type of planting geometry and plant sizes available in conventional planting as well as high density planting system (HDPS). A cotton stalk pulling unit consist of a pair of 400 mm long counter rotating tapered drums with 190 mm larger and 160 mm smaller section diameter, respectively (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Drums are mounted parallelly on a rigid frame. Both, drums are covered with 4, 8 and 12 mm thick rubber sheets of lengths 90, 163 and 147 mm respectively, so as to form variable drum clearance to produce three stage uprooting effect (Fig.\u0026nbsp;2). Three stage variable drum clearance arrangement provides enough opportunity to the standing stalks of different diameters to get uprooted in any of the three stages thus increases overall uprooting efficiency of the machine. Out of two tapered drums, one is fixed on frame while larger section of other drum is movable and consist of spring loaded drum clearence adjusting mechanism (Fig.\u0026nbsp;2 and Fig.\u0026nbsp;3). A spur gear with outer diameter of 210 mm and 28 number of teeths is fixed at larger end of both the drums such that both gears are in mesh with each other (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e and Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe larger end of one of the two counter-rotating drums is provided with spring loaded drum clearence adjusting mechanism (Fig.\u0026nbsp;2 and Fig.\u0026nbsp;3). Spring loaded drum clearence adjustment mechanism helps counter-rotating drums to adjust the clearence according to the thickness or size of entering cotton stalks thus extend the utility of machine for wide range of stalks sizes. A provision was also provided in drum clearence adjustment mechanism to set the machine for specific sizes of cotton stalks. The pressure with which counter-rotating drums are pushed against each other and drum clearence in running condition both are adjusted by adjusting the exposed length of compression spring with the help of pair of studs. When stalks of larger diameter enters the machine, the movable drum moves in outward direction while the spur gears still meshed (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). The maximum distance with which movable drum moves sideward is equal to the whole depth of gear. The compression spring present in drum clearence adjusting mechanism again press the movable drum to original position after uprooting of stalk. However, the machine was designed to uproot the stalks having maximum diameter of 25 mm. A side cover was also provided as a protective shield around the outer exposed portion of rotating drums (Fig.\u0026nbsp;3). It is designed such that, it shields overall exposed drum length, drum surface and rotating spur gear. A pair of crop row dividers are provided at the front side of the cotton stalk pulling unit (Fig.\u0026nbsp;3). The row dividers are positioned such that, it guides the standing crop and gathers widely spread branches into the clearence between rotating drums through stalk guide. A pair of crop stalk guide are provided in between crop row dividers and rotating drums to guide the gathered stalks from row dividers into variable clearence between counter rotating tapered drums (Fig.\u0026nbsp;3). The spring loaded stalk guides can adjust the opening as per the size of entering stalk material. The desired output of the machine at the end should be complete uprooting with the intact roots of the standing stalks with minimum breakage and miss as shown in Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eExperimental plan and statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe machine was tested for three independent variables namely forward speed of operation (3 levels; 1.37, 1.67 and 1.95 km/h), counter-rotating drum speed (4 levels; 250, 300, 350 and 400 rpm) and counter-rotating drum inclination (3 levels; 0\u003csup\u003e0\u003c/sup\u003e, 10\u003csup\u003e0\u003c/sup\u003e, 20\u003csup\u003e0\u003c/sup\u003e) for 30 m length of run as shown in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. The dependent variables such as total number of plants in a selected row, number of plants uprooted, number of plants broken, and number of plants left were recorded. The performance parameters such as uprooting effciency, percent plant broken and percent plant left were also computed (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eExperimental variables for optimization study of cotton stalk puller\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(i)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward speed, km/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(ii)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrum speed, rpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(iii)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrum inclination, deg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eUprooting efficiency (%); Plant broken (%); Plant left (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe analysis of the experimental data was done using statistical package SAS 9.3 at 5% level of significance by Tukey\u0026rsquo;s honest significant difference (HSD) test which is a statistical test and single-step multiple comparison procedure. The test compares the difference between each pair of means with suitable adjustment for the multiple testing. Simple two-way analysis of variance (ANOVA) was also done for dependent variables and p-value was used to analyse the effect of independent variables. Response surface methodology (RSM) using full factorial design data was used to optimise cotton stalk uprooting operation by the developed cotton stalk puller machine. The effect of counter-rotating drum speed, drum inclination and forward speed on uprooting efficiency and plants broken were investigated to enhance the performance of the machine on field.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003ePrediction model using Hybrid PSO- ANN Approach\u003c/h2\u003e\n \u003cp\u003eAn Artificial Neural Network (ANN) with multilayer feed forward back propagation architecture was developed for the counter-rotating cotton stalk puller in MATLAB 2019b. The network included tan-sigmoid activation for hidden layers and linear functions for output layers. The Levenberg-Marquardt algorithm facilitated model learning. The architecture featured seven input neurons, six output neurons, and a hidden layer configured with 7 and 13 neurons, resulting in a 3-7-13-3 structure. Training used 36 data sets, with 90% for training and 10% for testing. The ANN achieved an R\u0026sup2; value of 0.942, indicating strong prediction accuracy. Performance evaluation using R\u0026sup2;, RMSE, and RPD validated the model\u0026apos;s reliability and precision, with an R\u0026sup2; of 0.942 showing a strong correlation between predicted and actual values and a low RMSE indicating minimal prediction errors as per Eqs.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e\u0026ndash;\u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{R}^{2}=1-\\frac{\\sum\\:_{i=1}^{N}{\\left({y}_{ai}-{y}_{pi}\\right)}^{2}}{\\sum\\:_{i=1}^{N}{\\left({y}_{ai}-\\stackrel{-}{{y}_{a}}\\right)}^{2}}$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:RMSE\\:=\\sqrt{\\frac{\\sum\\:_{i=1}^{N}{\\left({y}_{ai}-{y}_{pi}\\right)}^{2}}{N}}$$\u003c/div\u003e\n \u003cdiv\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ3\"\u003e\n \u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:RPD\\:=\\frac{100}{N}\\sum\\:_{i=1}^{N}\\frac{\\left|({y}_{ai}-{y}_{pi}\\right|}{\\left|{y}_{ai}\\right|}\\:$$\u003c/div\u003e\n \u003cdiv\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, N represent the number of datasets, \u003cspan\u003e\u003cspan\u003e\\(\\:{y}_{ai}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan\u003e\u003cspan\u003e\\(\\:{y}_{pi}\\)\u003c/span\u003e\u003c/span\u003edenote the actual and predicted output values of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e set, respectively, and \u003cspan\u003e\u003cspan\u003e\\(\\:\\stackrel{-}{{y}_{ai}}\\)\u003c/span\u003e\u003c/span\u003erepresents the mean of actual output values.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eOptimization using hybrid ANN and PSO\u003c/h2\u003e\n \u003cp\u003eHybrid Artificial Neural Network (ANN) combined with a Particle Swarm Optimization (PSO) algorithm was developed in MATLAB 2019b to optimize the operational parameters of cotton stalk puller using the process shown in Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e. The initial phase involved training a 3-7-13-3 feed-forward backpropagation ANN with experimental data. After testing for reliability, the ANN mapped the operational parameters of the cotton stalk puller. In the second phase, these parameters were refined using an enhanced PSO algorithm. Two PSO variations were used namely, standard PSO with a constant inertia weight (\u0026omega;) and Improved PSO with a linearly decreasing inertia weight and confined search space for better optimization and convergence. The study focused on inertia weight for PSO parameter selection. The hybrid ANN-PSO technique integrated the Improved PSO algorithm, with the ANN to enhance optimization outcomes (Sankar et al., \u003cspan\u003e2014\u003c/span\u003e). The fitness function for the PSO was defined by the error sum-of-squares between required and ANN-predicted parameters, guiding the PSO to minimize this error and optimize the cotton stalk puller\u0026apos;s performance as shown in Eq.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv id=\"Equ4\"\u003e\n \u003cdiv id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:F=\\sum\\:_{i=1}^{0}{\\left({y}_{ri}-\\:{y}_{pi}\\right)}^{2}$$\u003c/div\u003e\n \u003cdiv\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, F is fitness function, \u003cspan\u003e\u003cspan\u003e\\(\\:{y}_{ri}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan\u003e\u003cspan\u003e\\(\\:{y}_{pi}\\)\u003c/span\u003e\u003c/span\u003e are required and predicted i\u003csup\u003eth\u003c/sup\u003e output parameter respectively, and there is \u003cem\u003eO\u003c/em\u003e number of output parameters as in present study \u003cem\u003eO\u003c/em\u003e is 3. In the Improved PSO algorithm, the swarm has 100 particles over 1000 iterations. The cognitive parameters c1 and c2 are set to 2. The inertia weight factor \u0026omega; decreases linearly from 0.9 to 0.4. Positions of swarm particles are constrained within specified bounds.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003ePerformance evaluation\u003c/h2\u003e\n \u003cp\u003eThe cotton stalk puller was mounted at front of the tractor with the help of independent hitching system. The hitching system could also lift or lower down the machine during transportation and adjust the pulling height during operation in the field. The testing of experimental cotton stalk puller machine was carried out for different treatment combinations of three independent variables namely drum speed (250, 300, 350 and 400 rpm), drum inclination (0\u003csup\u003eo\u003c/sup\u003e, 10\u003csup\u003eo\u003c/sup\u003e and 20\u003csup\u003eo\u003c/sup\u003e) and forward speed (1.37, 1.67 and 1.95 km/h) as stated in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. The optimization study was conducted at experimental field of ICAR-Central Institute of Agricultural Engineering, Bhopal, India. The cotton crop was grown at plant spacing of 0.60 x 0.75 m followed by majority of cotton farmers in India. The details of soil and crop condition at the time of experiment is given in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. Each experimental run was conducted by operating the machine for 30 m length of row at selected treatment combinations and performance indicators such as number of plants uprooted, plants broken and plants left were recorded (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCrop and soil conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCrop conditions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays after harvest, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrop geometry, m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6 x 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStubble dimensions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStubble height, m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem diameter, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot length, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247.8\u0026thinsp;\u0026plusmn;\u0026thinsp;87.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack cotton\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoisture content, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCone index, kPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2680\u0026thinsp;\u0026plusmn;\u0026thinsp;927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eUprooting efficiency is an important indicator showing the intended performance of machine. During each test run for selected treatment combination, total number of plants were counted before operating a machine on selected row length of 30 m and number of completely uprooted plants were recorded. Uprooting efficiency is calculated as the number of completely uprooted plants to the total number of plants present in the sample area using Eq.\u0026nbsp;(5) (Solanki and Yadav, \u003cspan\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eUprooting efficiency (%) =\u003cspan\u003e\u003cspan\u003e\\(\\:\\:\\:\\:\\:\\frac{Number\\:of\\:completely\\:uprooted\\:plants}{Total\\:number\\:of\\:plants\\:}\\)\u003c/span\u003e\u003c/span\u003e x 100% (5)\u003c/h2\u003e\n \u003cp\u003eOut of the total number of plants selected in each experimental run, some stalks could not be uprooted and got broken due to crushing in counter-rotating drums or in set of gears. Number of such broken plants were recorded and shown as percent plant broken using Eq.\u0026nbsp;(6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003ePlants broken (%) =\u003cspan\u003e\u003cspan\u003e\\(\\:\\:\\:\\:\\:\\frac{Number\\:of\\:broken\\:plants\\:}{Total\\:number\\:of\\:plants\\:}\\)\u003c/span\u003e\u003c/span\u003e x 100% (6)\u003c/h2\u003e\n \u003cp\u003eBesides uprooted and broken plants, some plants among the selected plants could not enter into the machine due to its improper orientation. Number of such un-uprooted or left plants were recorded and shown as percent plant left using Eq.\u0026nbsp;(7).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003ePlants left (%) =\u003cspan\u003e\u003cspan\u003e\\(\\:\\:\\:\\:\\:\\frac{Number\\:of\\:plants\\:left}{Total\\:number\\:of\\:plants}\\)\u003c/span\u003e\u003c/span\u003e x 100% (7)\u003c/h2\u003e\n \u003cp\u003eThere was no benchmark for ideal uprooting efficiency of any stalk uprooting device as any such technology is not commercially available. For this study, the target for uprooting efficiency of the developed machine was set to be at least 90% to ensure maximum stalk to be removed from the field. Meanwhile, the broken plants should not be more than 10% of the total plants. However, the desired output from the machine should be complete uprooting of the stalks along with the entire root system without plant breakage and minimum plant left (Figs.\u0026nbsp;8 and 9).\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePerformance evaluation of cotton stalk puller machine\u003c/h2\u003e \u003cp\u003ePerformance result of cotton stalk puller machine for selected independent variables is given in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e in the form of mean values of uprooting efficiency, percent plant broken, and percent plant left. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e also provides a pairwise comparison of these dependent performance variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of cotton stalk puller at different variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUprooting efficiency,(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlant Broken,(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlant left,(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1 (250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.96 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.85 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 (300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.38 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.72 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3 (350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.90 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.33 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN4 (400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.21 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.84 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI1 (0\u003csup\u003e0\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.42 \u003csup\u003eAB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.56 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI2 (10\u003csup\u003e0\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.86 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.28 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90 \u003csup\u003eAB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI3 (20\u003csup\u003e0\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.55 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.20 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.62\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.40 \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS2 (1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.13 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.06 \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS3 (1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.09\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.59\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.84 \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote : N (RPM), I(degree), S(km/h) # Data are mean values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDifferent letters indicate significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) by Tukey\u0026rsquo;s test within the same row\u003c/p\u003e \u003cp\u003eThe mean uprooting efficiency was maximum i.e. 91.90% at drum speed of 350 rpm while it was minimum (86.21%) at 400 rpm drum speed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Because at heigher drum speed number of broken plants were increased. At 10\u003csup\u003e0\u003c/sup\u003e drum inclination, about 90.86% plants were uprooted, which was highest as compared to 0\u003csup\u003e0\u003c/sup\u003e (89.42%) and 20\u003csup\u003e0\u003c/sup\u003e (88.55%). Uprooting efficiency was also influenced by forward speed of operation i.e. with the increase in travel speed uprooting efficiency decreased. It was maximum (93.62%) at speed 1.37 km/h as compared to 90.13% and 85.09% at 1.67 km/h and 1.95 km/h speed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Uprooting efficiency were significantly affected by drum speed and forward speed of travel at 1% level of significance while it was affected significantly at 5% level of significance by drum inclination (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A two-row cotton stalk pulling machine developed by Yumak and Evcim (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) also had an uprooting efficiency of 95% in good conditions.\u003c/p\u003e \u003cp\u003ePlant broken was lowest (7.33%) at drum speed of 350 rpm whereas it was maximum i.e. 11.84% at 400 rpm drum speed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Plants broken was significantly affected by drum speed at 1% level of significance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). About 7.28% plants were broken at drum inclination of 10\u003csup\u003e0\u003c/sup\u003e which was minimum as compared to 9.56% and 9.20%, respectively at 0\u003csup\u003e0\u003c/sup\u003e and 20\u003csup\u003e0\u003c/sup\u003e. The forward speed of travel also significantly affected plants broken at 1% level of significance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which was increased with the increase in forward speed. Plant broken was significaltly lower (5.40%) at 1.37 km/h forward speed as compared to 8.06% and 12.59%, respectively at 1.67 km/h and 1.95 km/h forward speed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Yumak and Evcim (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) reported 2% and 6% broken stalks and stalks which were not pulled, respectively, in two row cotton stalk pulling machine.\u003c/p\u003e \u003cp\u003ePlants left are nothing but the plants which were not able to pass through the crop row divider and crop guide to the clearence between two counterrotating drums. It is minimum at drum speed 350 rpm, at drum inclination 0\u003csup\u003e0\u003c/sup\u003e and forward speed of 1.37 km/h (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, plants left is mainly dependent upon the crop orientation from vertical and skill of the operator using the machine. Tractor driver could not follow the standing cotton crops at heigher forward speed which leads to increase in plants left to 2.84% at 1.95 km/h forward speed. The percent plants left was independent i.e. was not significantly affected by drum speed, drum inclination and forward speed of travel (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ep-values from ANOVA of uprooting efficiency, plants broken and plants left\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUprooting Efficiency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlants broken\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlants left\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInclination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0055*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0490*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPM*Inclination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPM*Speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInclination*Speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPM*Inclination*Speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e(Note: **Significant at 1%; *Significant at 5%) DF : Degree of freedom\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of performance parameters using RSM\u003c/h2\u003e \u003cp\u003eResponse Surface Methodology (RSM) using full factorial design data was used to optimise the plants uprooted to be maximum and plants broken to be mimimum by the cotton stalk puller machine. The effect of counter-rotating drum speed, drum inclination and forward speed of operation on performance parameters such as uprooting efficiency, plant broken and plants left were investigated in order to enhance the uprooting efficiency of the machine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of stalk uprooting efficiency\u003c/h2\u003e \u003cp\u003eThe optimization of uprooting efficiency by the cotton stalk puller showed the optimum values of 332.512 rpm, 8.362\u0026deg; and 1.37 km/h for counter-rotating drum speed, drum inclination and forward speed of operation, respectively. However, the response variable uprooting efficiency has an optimal value of 96.59% with individual and combined desirability of 0.97 and 0.88 which shows the closeness of the predicted values to the observed values. Analysis of variance for regression model obtained for optimization of uprooting efficiency of cotton stalk puller is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe significance of model is indicated by its F-value of 24.54. The probability of an F-value this large occurring due to noise is merely 0.01%. The model established is significant as characterized by F-value which indicates that at least one of the independent variables contributed to response seen in uprooting efficiency. Uprooting efficiency was significantly affected by drum speed and forward speed as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It demonstrates that the effect of distinction of independent parameters on the uprooting efficiency is not by chance. Regression model for relationship between uprooting efficiency and selected independent variables is presented in Eq.\u0026nbsp;(8).\u003c/p\u003e \u003cp\u003eUE\u0026thinsp;=\u0026thinsp;93.43\u0026ndash;1.46*\u003cem\u003eDS\u003c/em\u003e \u0026minus;\u0026thinsp;0.43*\u003cem\u003eI \u0026minus;\u0026thinsp;4\u003c/em\u003e.26*\u003cem\u003eFS\u003c/em\u003e -0.0106*\u003cem\u003e(DS)\u003c/em\u003e*\u003cem\u003e(I)\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.11*\u003cem\u003e(DS)\u003c/em\u003e*\u003cem\u003e(FS)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e-0.12*\u003cem\u003e(I)\u003c/em\u003e*\u003cem\u003e(FS)\u003c/em\u003e -3.44*\u003cem\u003eDS\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e -1.87*\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e- 0.9153*\u003cem\u003eFS\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.78) (8)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWhere,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eUE\u0026thinsp;=\u0026thinsp;Uprooting efficiency, DS\u0026thinsp;=\u0026thinsp;Drum speed, I\u0026thinsp;=\u0026thinsp;Drum Inclination, FS\u0026thinsp;=\u0026thinsp;Forward Speed\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe co-efficient of determination (R\u003csup\u003e2\u003c/sup\u003e) was found to be 0.78. When developing a statistical model for optimization, an R\u003csup\u003e2\u003c/sup\u003e value larger than 0.75 is appropriate. (Peng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wondi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The findings suggest that 78% of the overall variability in the uprooting efficiency of cotton stalk puller can be attributed to variations in the independent variables. However, the regression model in Eq.\u0026nbsp;8 was found to be adequate, as evidenced by the insignificant lack of fit. Some plants with more stem and root diameter than designed value was crushed and broke, which contributed to the lower uprooting efficiency.\u003c/p\u003e \u003cp\u003eThe interactions between independent variables and uprooting efficiency are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e12\u003c/span\u003e. Uprooting efficiency to increase as the drum speed increases up to 310 rpm and then later on it starts decreasing with the increase in drum speed beyond 310 rpm as revealed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e11\u003c/span\u003e. This is because at higher drum speed some thinner stalks starts breaking due the friction from harder rubber coatings and application of more power than required. However, the uprooting efficiency decreased with increase in forward speed of operation as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e12\u003c/span\u003e. With the increase in forward speed, the plant could not enter upright in the clearance between rotating drums and starts breaking without getting uprooted. Counter-rotating drums also do not get enough time to hold and uproot the plants which increased the plants left. The maximum uprooting efficiency obtained was around the 10\u003csup\u003e0\u003c/sup\u003e of drum inclination as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e12\u003c/span\u003e. At perfect horizontal orientation of the drums i.e. at 0\u003csup\u003e0\u003c/sup\u003e, plant cannot stand upright and tends to inclined towards direction of travel due to minimum drum-stem contact which results in more breakage of the stem. However; there is more drum-stem contact area at 20\u003csup\u003e0\u003c/sup\u003e inclination due to which the uprooted or broken stalks could not release properly in upright orientation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for model regression for uprooting efficiency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of\u003c/p\u003e \u003cp\u003eSquares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1199.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2049\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e871.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e871.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS*I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9813\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS*FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8081\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI*FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7703\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0021\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1218\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5517\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrelation Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1536.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**Significant at 5% level; *Not significant at 5% level ; DF : Degree of freedom\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of plant broken\u003c/h2\u003e \u003cp\u003eThe optimum values of 317.5 rpm, 9.33\u003csup\u003eo\u003c/sup\u003e, and 1.37 km/h were obtained for drum speed, drum inclination and forward speed of operation while the response variable plants broken was found to be minimum i.e. 2.38%, at a desirability of 0.85 which indicates the nearness of the predicted values to the observed values. The results of the analysis of variance as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, which proved the statistical significance of the regression model relating the plants broken to the drum speed, drum inclination and forward speed of operation at 95% confidence level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The F-value 9.41 of model suggests that it is significant. The chance of an F-value this large occurring due to noise is merely 0.01%. As indicated by the F-value, the developed model is significant, suggesting that at least one of the independent variables contributed to the response seen in broken plants. Plants broken were significantly influenced by drum speed and forward speed of operation as given in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The regression model for the relationship between plants broken and the independent variables is presented in Eq.\u0026nbsp;(9). The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) was found to be 0.58. This indicates that 58% of the total variability in the plants broken by the cotton stalk pulling machine can be attributed to variations in the independent variables. The lack of fit is not statistically significant as demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, implying that the regression model is adequate in Eq.\u0026nbsp;(9).\u003c/p\u003e \u003cp\u003ePB\u0026thinsp;=\u0026thinsp;5.09\u0026thinsp;+\u0026thinsp;1.74*\u003cem\u003eDS\u003c/em\u003e \u0026minus;\u0026thinsp;0.1669*\u003cem\u003eI\u0026thinsp;+\u003c/em\u003e\u0026thinsp;3.60*\u003cem\u003eFS\u0026thinsp;+\u003c/em\u003e\u0026thinsp;0.2063*\u003cem\u003e(DS)\u003c/em\u003e*\u003cem\u003e(I)\u003c/em\u003e- 0.2815*\u003cem\u003e(DS)\u003c/em\u003e*\u003cem\u003e(FS)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e-0.2710*\u003cem\u003e(I)*(FS)\u003c/em\u003e\u0026thinsp;+\u0026thinsp;2.61*\u003cem\u003eDS\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;2.10*\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;1.06*\u003cem\u003eFS\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.58 (9)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWhere,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePB - Plants broken, DS - Drum speed, rpm; I - Drum inclination, deg; FS - Forward speed, km/h\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe interactions between plants broken and independent variables are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e15\u003c/span\u003e. The plants broken increased with increase in both forward speed and drum speed beyond 310 rpm as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e14\u003c/span\u003e. Plant broken was minimum at forward speed 1.37 km/h and increased as the speed increased upto 1.95 km/h (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e14\u003c/span\u003e). The increase in the plant broken with forward speed could be due to lack of sufficient time available for the stalks to get uprooted due to the counter-rotating action of the drum and contact with the rubber. Due to faster forward motion of the machine, the standing stalk gets broken into the pair of spur gears or gets tilted in the direction of travel and broken from the weaker section of the stem. On the other hand, at very high drum speeds, the stalks with smaller diameters gets rubbed at very high intensity and breaks. Therefore, plant broken increased with increase in drum speed beyond 310 rpm. However, plant broken was minimum around 10\u003csup\u003e0\u003c/sup\u003e of drum inclination and beyond which it increased in both direction towards 0\u003csup\u003eo\u003c/sup\u003e and 20\u003csup\u003eo\u003c/sup\u003e as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e15\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for model regression for plants broken\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of\u003c/p\u003e \u003cp\u003eSquares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e931.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0015\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7285\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e621.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e621.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS*I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7490\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS*FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6626\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI*FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6454\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0042\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0137\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2063\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e681.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6846\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e425.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCor Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1612.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**Significant at 5% level; *Not significant at 5% level\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDF : Degree of freedom\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of percent plant left\u003c/h2\u003e \u003cp\u003eThe counter-rotating drum speed, drum inclination, and forward speed of operation were found to have optimal values of 339.27 rpm, 3.68\u0026deg;, and 1.37 km/h. The response factor, on the other hand, had an optimal value of 0.67% for plant left with a desirability of 0.89, indicating the closeness of the predicted values to the observed values.\u003c/p\u003e \u003cp\u003eThe findings for the Analysis of variance of regression model relating the plant left to the independent variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The results from the analysis of variance test indicated that the regression model in Eq.\u0026nbsp;(10) was statistically significant at a 95% confidence level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The obtained regression model is not significant, as depicted by the model F-value of 4.06. The probability of an F-value this large occurring due to noise is only 1.03%, indicating that the independent variables have the least impact on the observed responses in terms of the percentage of plants left. The regression model for the relationship between plants left and the independent variables is presented in Eq.\u0026nbsp;(10). The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) was found to be 0.15. The variation in the independent variables is demonstrated to explain 15% of the total responses observed in the plants left by the machine. The selected independent variables have least effect on response variable plants left. Though it is selected into the study because it indirectly affects the uprooting efficiency. Lack of fit was also not significant as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The low co-efficient of determination indicated the inadequacy of the regression model in Eq.\u0026nbsp;(10) although the lack of fit was insignificant.\u003c/p\u003e \u003cp\u003ePL\u0026thinsp;=\u0026thinsp;2.05\u0026ndash;0.2780*\u003cem\u003eDS\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.8837*\u003cem\u003eI\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.7599*FS \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.15 (10)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWhere,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePL - Plants left, DS - Drum speed, I - Drum Inclination, FS - Forward Speed\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFigures\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e18\u003c/span\u003e show the interactions between plants left and independent variables. The drum speed has the least effect on the plants left while the drum inclination and forward speed significantly affect the plants left. However, the plants left increased with increase in the drum inclination (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e16\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e18\u003c/span\u003e) and forward speed of operation (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e18\u003c/span\u003e). Moreover, the plant left is purely dependent upon the skill of the driver operating the machine which eventually gets reduced with the enhanced skill of the operator. The vertically oriented plants can easily enter into the variable clearance of the machine. However, not all plants in the field are perfectly oriented vertically which may get skipped from entering into the machine and left due to insufficient time available at higher forward speeds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for model regression for plants left\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0103\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4605\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0119\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0296\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e381.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7295\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCor Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e450.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**Significant at 5% level; *Not significant at 5% level\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDF : Degree of freedom\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation of RSM models\u003c/h2\u003e \u003cp\u003eThe study generated two different optimal conditions from RSM model and experimental results for maximum uprooting efficiency and minimum plants broken. When comparing the two, the best scenario to adopt is derived from the RSM model, with the main focus being on enhancing the uprooting efficiency. Because, the uprooting efficiency was the most important among all selected response variable under the study. Therefore, the ultimate aim of the study was to maximize the uprooting efficiency of the machine. The RSM model takes into account not only the intended result, uprooting efficiency, but also the critical factor of minimizing plant breakage. The RSM model's conditions are carefully chosen to strike a balance between maximizing uprooting efficiency and minimizing plant broken. Plant broken can have major agronomic and operational implications. This choice is based on a comprehensive strategy, recognizing that finding the perfect equilibrium among various elements, such as efficiency, can lead to better outcomes overall in the scenario of removing cotton stalks from the field.\u003c/p\u003e \u003cp\u003eThe models obtained for uprooting efficiency, plants broken and plants left by cotton stalk puller machine were validated by comparing experimental values with the predicted test values. The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) for uprooting efficiency, plants broken and plants left were 0.78, 0.58 and 0.15, respectively. Furthermore, there was a strong correlation between the estimated and experimental data. A normal probability of residuals was also observed to understand the nature of fitting. The fitting of regression data indicated that it is nearby a straight line, representing that error terms are normally distributed. Consequently, the hypothesis of analysis is met. The second order polynomial model fits the relationship between uprooting efficiency and plants broken, based on the F-value and R\u003csup\u003e2\u003c/sup\u003e. The plots between the predicted and observed values revealed high degree of agreement between the predicted and observed values for uprooting efficiency and plants broken. This showed a strong correlation between the experimental and predicted values of plants broken and uprooting efficiency as shown in Figs.\u0026nbsp;19\u0026ndash;20. It demonstrated how well the data fit the model and provided a strong estimate of the response for the uprooting efficiency and plants broken in the range of independent variables under study. However, the correlation between the observed and predicted values of plants left were low as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e21\u003c/span\u003e. The experimental values for uprooting efficiency, plants broken, plants left were 96.59, 2.38 and 0.67% respectively within the range of 1.37\u0026ndash;1.95 km/h for forward speed, 250\u0026ndash;400 rpm for counter-rotating drum speed and 0\u0026ndash;20\u0026deg; for drum inclination under optimal conditions of 1.37 km/h, 332.512 rpm and 8.362\u0026deg; for uprooting efficiency; 1.37 km/h, 317.54 rpm and 9.33\u0026deg; for plants broken and 1.37 km/h, 339.27 rpm and 3.68\u0026deg; for plants left for forward speed, counter-rotating drum speed and drum inclination, respectively. The deviations between predicted and observed values of uprooting efficiency, ranged from 0.05\u0026ndash;3.03, 0.07\u0026ndash;2.88 and 0.12\u0026ndash;1.80 for uprooting efficiency, plants broken and plants left respectively. The adequacy of models established in describing the observed data and closeness of the observed values to the predicted values were also demonstrated by the desirability values of 0.97, 0.85, and 0.89 of various response variables as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e22\u003c/span\u003e. The models obtained for the cotton stalk uprooting operation adequately described the observations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eOptimization using Hybrid PSO-ANN algorithm\u003c/h2\u003e \u003cp\u003eOptimization of cotton stalk puller performance parameters was achieved by integrating the top-performing prediction model with Particle Swarm Optimizer (PSO). The PSO effectively explored performance parameters using an inertia weight of 0.9, acceleration coefficients of 2, a swarm size of 100, and 1000 iterations. The convergence curve revealed that the mean fitness values approximated the global best for uprooting efficiency, plant broken, and plant left, by iterations 985, 989, and 997, respectively, which indicates convergence towards optimal solutions (Fig.\u0026nbsp;19). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the optimal parameters from the ANN-PSO model, with forward speed at 1.37 km/h, drum inclination at 7.89 degrees, and drum speed at 331.45. This integration of prediction models with optimization algorithms exemplifies a sophisticated approach to enhancing cotton stalk puller performance. The convergence curve analysis highlighted the gradual improvement in fitness values, with mean and global best fitness values closely aligning, demonstrating the PSO's effectiveness. The optimized parameters maximize plant uprooting efficiency while minimizing plant broken and plant left showcasing a systematic method for fine-tuning mechanical systems in agricultural applications. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e also shows the deviation or percentage error of 1.94% among observed and predicted values of uprooting efficiency. The results are reliable and adequate when percentage error is below 15% (Wondi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimized performance parameters by ANN-PSO\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003cp\u003eSpeed, (km/h)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclination\u003c/p\u003e \u003cp\u003eAngle, (deg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrum speed, (rpm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePlant Uprooting efficiency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeviation (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe optimization of a counter-rotating cotton stalk pulling machine revealed the optimum values for operational and design parameters such as forward speed, counter-rotating drum speed and counter-rotating drum inclination. The performance of machine such as uprooting efficiency, plants broken and plants left were significantly affected by the selected parameters based on the findings of the study. The optimal values of 332.5 rpm, 8.36 degrees and 1.37 km/h were obtained for drum speed, drum inclination and forward speed, respectively for the selected performance parameters using RSM. However, the plants uprooted, plants broken and plants left showed the optimum values of 96.59%, 2.81% and 1.11%, respectively. The uprooting efficiency and plants broken were highly significantly affected by drum speed and forward speed of travel.\u003c/p\u003e \u003cp\u003eANN-PSO model provided 1.37 km/h forward speed, 7.89\u003csup\u003e0\u003c/sup\u003e drum inclination and 331.45 rpm counter-rotating drum speed as optimal parameters with only 1.94% error in the observed and predicted values of uprooting efficiency. The developed machine achieved the expected uprooting efficiency of over 90% with less than 10% of plants broken. In short both, the RSM and combined ANN-PSO approach can better predict and optimize cotton stalk puller performance. Both, methods have given nearly same results, therefore, either of the two methods can be used for optimization of machine operation. The models acquired for the stalk uprooting operation were deemed sufficient to explain the results. The findings of study would provide valuable information for operation of developed machine under different operating conditions and other deep-rooted crops as well. It also offers valuable information for integrating the developed machine with available mobile stalk shredders to get complete solution of uprooting and shredding of cotton stalks in one operation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinistry of Agricultural, GOI, and ICAR-Central Institute of Agricultural Engineering, Bhopal supported this research project. The authors also express their gratitude to ICAR-Central Institute for Cotton Research, Nagpur for their technical advisory support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAshutosh P Pandirwar:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing original draft, Data curation, Formal analysis. \u003cstrong\u003eHimanshu S Pandey:\u0026nbsp;\u003c/strong\u003eWriting review, Project administration \u0026amp; editing. \u003cstrong\u003eAjit Magar:\u003c/strong\u003e Conceptualization, Data collection. \u003cstrong\u003eAjay K Roul:\u0026nbsp;\u003c/strong\u003eSupervision, Project administration. \u003cstrong\u003eManoj Kumar:\u0026nbsp;\u003c/strong\u003eFormal analysis, Supervision. \u003cstrong\u003eBikram Jyoti:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Formal analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be provided on request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors hereby certify that they have no known competing financial interest or personal relationships that could have appeared to influence the research work reported in this publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl Afif R, Pfeifer C, Pr\u0026ouml;ll T. Bioenergy recovery from cotton stalk. In: Mahmood-ur-Rahman Ansari (ed) Advances in Cotton Research. Intech Open. 2019; 1\u0026ndash;19. https://doi.org/10.5772/intechopen. 88005\u003c/li\u003e\n\u003cli\u003eAnantachar M, Kumar PG, Guruswamy T. Neural network prediction of performance parameters of an inclined plate seed metering device and its reverse mapping for the determination of optimum design and operational parameters. Comput. Electron. Agric. 2010; 72(2): 87-98. Available from: https://doi.org/10.1016/j.compag.2010.03.001 \u003c/li\u003e\n\u003cli\u003eCai J, Zhang J, Gao Z, et al. Design and test of a wheel-belt type cotton stalk puller. Int. J. Agric. Biol. Eng. 2024; \u003cem\u003e17\u003c/em\u003e(2): 102-108. DOI: 10.25165/j.ijabe.20241702.7287\u003c/li\u003e\n\u003cli\u003eDirectorate of Economics and Statistics (DES), Second Advance Estimates of Production of Foodgrains, Oilseeds and other Commercial Crops for the 2023-24. Department of Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture and Farmers\u0026rsquo; Welfare, Government of India, New Delhi. 2024. https://desagri.gov.in/wp-content/uploads/2024/03/Time-Series-Production-2nd-AE-2023-24-English.pdf. Accessed 26 Apr 2024.\u003c/li\u003e\n\u003cli\u003eFawzy S, Osman AI, Farrell C, et al. Characterization and kinetic modelling for pyrolytic conversion of cotton stalks. Energy Sci. Eng. 2021; \u003cem\u003e9\u003c/em\u003e(10): 1908-1918. https://doi.org/10.1002/ese3.961\u003c/li\u003e\n\u003cli\u003eFood and Agricultural Organization STAT (FAOSTAT). Crops and livestock products. 2022. https://www.fao.org/faostat/en/#data/QCL. Accessed 26 Apr 2024. \u003c/li\u003e\n\u003cli\u003eHuang W, Bai Z, Hoefel D, et al. Effects of cotton straw amendment on soil fertility and microbial communities. Front. Environ. Sci. Eng. 2012; 6: 336-349. https://doi.org/10.1007/s11783-011-0337-z\u003c/li\u003e\n\u003cli\u003eICAC. Cotton this month for the month of April 2023, International Cotton Advisory Committee (ICAC) (WWW document). 2023. URL (https://icac.org/Content/PublicationsPdf%20Files/baa65705_ad54_41d7_bd5d_ad89ec88df6b/CTM_2023_04_03.pdf.pdf) (accessed 13.06.2024)\u003c/li\u003e\n\u003cli\u003eKhan AA, Umair S, Rudra RP, et al. Structural analysis of cotton stalk Puller and Shredder Machine. Alexandria Eng. J. 2023; 64: 335\u0026ndash;347 https://doi.org/10.1016/j.aej.2022.09.002\u003c/li\u003e\n\u003cli\u003eKumar GP, Srivastava B, Nagesh DS. Modelling and optimization of parameters of flow rate of paddy rice grains through the horizontal rotating cylindrical drum of drum seeder. Comput. Electron. Agric. 2009; 65(1): 26-35. Available from: https://doi.org/10.1016/j.compag.2008.07.006\u003c/li\u003e\n\u003cli\u003eMeng Q, Gu J, Xu Z, et al. Comparative analysis of genome sequences of the two cultivated tetraploid cottons, Gossypium hirsutum (L.) and G. barbadense (L.), Ind. Crops Prod. 2023; 196: 116471. https://doi.org/10.1016/j.indcrop.2023.116471 \u003c/li\u003e\n\u003cli\u003ePandirwar AP, Khadatkar A, Mehta CR, et al. Technological advancement in harvesting of cotton stalks to establish sustainable raw material supply chain for industrial applications: A review. BioEnergy\u003cem\u003e \u003c/em\u003eRes. 2023a; \u003cem\u003e16\u003c/em\u003e(2): 741-760. https://doi.org/10.1007/s12155-022-10520-3 \u003c/li\u003e\n\u003cli\u003ePandirwar AP, Pandey HS, Magar AP, et al. Physical, chemical, thermal, and mechanical properties of cotton stalk: An industrial multi-purpose cotton by-product. J. Agric. Eng. 2023b; 60(2): 188-204. DOI: 10.52151/jae2023602.1807\u003c/li\u003e\n\u003cli\u003ePareek CM, Tewari VK, Machavaram R. Multi-objective optimization of seeding performance of a pneumatic precision seed metering device using integrated ANN-MOPSO approach. Eng. Appl. Artif. Intell. 2023; \u003cstrong\u003e117\u003c/strong\u003e: 105559. Available from: https://doi.org/10.1016/j.engappai.2022.105559 \u003c/li\u003e\n\u003cli\u003ePareek CM, Tewari VK, Machavaram R, et al. Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ANN-PSO approach. Artif. Intell. Agric. 2021; \u003cem\u003e5\u003c/em\u003e: 1-12. Available from: https://doi.org/10.1016/j.aiia.2020.11.002\u003c/li\u003e\n\u003cli\u003ePeng WL, Mohd-Nasir H, Setapar SHM, et al. Optimization of process variables using response surface methodology for tocopherol extraction from Roselle seed oil by supercritical carbon dioxide. Ind. Crops Prod. 2019; 143: 111886 https://doi.org/10.1016/j.indcrop.2019.111886.\u003c/li\u003e\n\u003cli\u003ePothecarey BP, Ofield RJ. Destruction of old cotton for pest and disease control. World Crops. 1968; 20(8): 39-43. \u003c/li\u003e\n\u003cli\u003eRaj JVA, Kumar RP, Vijayakumar B, et al. Modelling and process optimization for biodiesel production from Nannochloropsis salina using artificial neural network. Bioresour. Technol. 2021. \u003cem\u003e329\u003c/em\u003e: 124872. https://doi.org/10.1016/j.biortech.2021.124872\u003c/li\u003e\n\u003cli\u003eRamanjaneyulu AV, Ramprasad B, Sainath N, et al. Crop residue management in cotton. Chron. of Bioresour. Management. 2021; \u003cem\u003e5\u003c/em\u003e(Mar, 1): 001-008.\u003c/li\u003e\n\u003cli\u003eSankar PA, Machavaram R, Shankar K. System identification of a composite plate using hybrid response surface methodology and particle swarm optimization in time domain. Measurement. 2014; \u003cem\u003e55\u003c/em\u003e: 499-511. Available from: https://doi.org/10.1016/j.measurement.2014.05.025 \u003c/li\u003e\n\u003cli\u003eSilverstein RA, Chen Y, Sharma-Shivappa RR, et al. A comparison of chemical pretreatment methods for improving saccharification of cotton stalks. Bioresour. Technol. 2007; 98:3000\u0026ndash;3011. https://doi.org/10.1016/j.biortech. 2006.10.022\u003c/li\u003e\n\u003cli\u003eSolanki HB, Yadav R. Development and performance evaluation of tractor operated plant uprooter for castor crop. Agric. Mech. in Asia, Africa and Latin America, 2009; 40(2): 41-46. \u003c/li\u003e\n\u003cli\u003eSumner HR, Hewig RE, Monroe GE. Harvesting cotton plat residue for fuel. Trans. ASAE, 1984b; 27: 968-972.\u003c/li\u003e\n\u003cli\u003eSumner HR, Monroe GE, Hellwig GE. Design elements of a cotton plant puller. Trans. ASAE, 1984a; 27: 366-369.\u003c/li\u003e\n\u003cli\u003eSutaria GS, Vora VD, Vekaria PD, et al. Technology for rapid composting of cotton stalk. Int J Agric Sci Res, 2016; 6:211\u0026ndash;216\u003c/li\u003e\n\u003cli\u003eTao Y, Wu D, Zhang QA, et al. Ultrasound-assisted extraction of phenolics from wine lees: Modeling, optimization and stability of extracts during storage. Ultrason. Sonochem. 2014; 21(2): 706-715. DOI: 10.1016/j.ultsonch.2013.09.005\u003c/li\u003e\n\u003cli\u003eTaoufik N, Boumya W, Elmoubarki R, et al. Experimental design, machine learning approaches for the optimization and modeling of caffeine adsorption. Mater. Today Chem. 2022; \u003cem\u003e23\u003c/em\u003e: 100732. https://doi.org/10.1016/j.mtchem.2021.100732\u003c/li\u003e\n\u003cli\u003eThe Cotton Corporation of India (CCI), Statistics. 2024. https://cotcorp.org.in/statistics.aspx. Accessed 26 Apr 2024.\u003c/li\u003e\n\u003cli\u003eWendel JF, Grover CE. Taxonomy and evolution of the cotton genus, Gossypium. Cotton, 2015; 57: 25-44. https://doi.org/10.2134/agronmonogr57.2013.0020\u003c/li\u003e\n\u003cli\u003eWondi MH, Haris NIN, Shamsudin R, et al. Development and testing of an oil palm (Elaeis guineensis Jacq.) fruit digester process for kernel free in crude palm oil production. Ind. Crops Prod. 2024; \u003cem\u003e208\u003c/em\u003e: 117755. https://doi.org/10.1016/j.indcrop.2023.117755 \u003c/li\u003e\n\u003cli\u003eXue Z, Fu J, Fu Q, et al. Modelling and optimizing the performance of green forage maize harvester header using a combined Response Surface Methodology\u0026ndash;Artificial Neural Network Approach. Agric. 2023; \u003cem\u003e13\u003c/em\u003e(10): 1890. https://doi.org/10.3390/agriculture13101890\u003c/li\u003e\n\u003cli\u003eYumak H, Evcim \u0026Uuml;. 1990. A two-row cotton stalk pulling machine. International Congress on Mechanization and Energy in Agriculture. Proceedings of a conference held in Adana, Turkey, 1-4 October 1990. 1990; 416-425 ref.13\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cotton stalk, Cotton mechanization, Uprooting efficiency, Stalk pulled","lastPublishedDoi":"10.21203/rs.3.rs-4874230/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4874230/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCotton stalks, a by-product left after cotton picking, have several industrial applications as a raw material. However, due to deep taproot system, the uprooting and disposal of cotton stalks from the field is a labour-intensive operation. In this study, the uprooting efficiency of a counter-rotating drum type cotton stalk puller (CSP) was optimized using Response Surface Methodology (RSM) and combined Artificial Neural Network (ANN) - Particle Swarm Optimization (PSO) approach. Machine operational parameters and design parameter were independent variables, whereas, uprooting efficiency, plants broken and plants left were response variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAn experimental CSP unit was operated in field at three forward speeds (1.37, 1.67 and 1.95 km/h), four drum speeds (250, 300, 350 and 400 rpm) and three drum inclinations (0\u003csup\u003e0\u003c/sup\u003e, 10\u003csup\u003e0\u003c/sup\u003e, 20\u003csup\u003e0\u003c/sup\u003e). The optimization using RSM shown 332.5 rpm drum speed, 8.36\u003csup\u003e0\u003c/sup\u003e drum inclination and 1.37 km/h forward speed as optimal values. Plants uprooted, plants broken and plants left have optimum values of 96.6%, 2.8% and 1.1% with individual desirability of 0.97, 0.85 and 0.89 showing the closeness of responses to predicted values. ANN-PSO model shown optimal parameters as 1.37 km/h forward speed, 7.89\u003csup\u003e0\u003c/sup\u003e drum inclination and 331.45 rpm drum speed with the observed and predicted values of uprooting efficiency are 96.72% and 94.84%, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe results show that both RSM and combined ANN-PSO approach can better predict and optimize the performance of CSP with higher accuracy. Optimization study provide essential information on optimal combination of operating and design parameters for enhanced uprooting efficiency with minimum plant breakage.\u003c/p\u003e","manuscriptTitle":"Optimization of uprooting efficiency of counter-rotating cotton stalk puller for on-field operations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-25 14:55:31","doi":"10.21203/rs.3.rs-4874230/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a4a909b7-7367-4b24-8d60-596b9e56ffe5","owner":[],"postedDate":"September 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T06:11:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-25 14:55:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4874230","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4874230","identity":"rs-4874230","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}