Espousing Environmental Pollution Management and Control by Exploring the Bioenergy Properties of Coconut Shell Nanoparticles

Espousing Environmental Pollution Management and Control by Exploring the Bioenergy Properties of Coconut Shell Nanoparticles | 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 Espousing Environmental Pollution Management and Control by Exploring the Bioenergy Properties of Coconut Shell Nanoparticles Ruhiya Abubakar, N’da Comoe Axel Aymeric, Amevi Acakpovi, Solomon Nsor Anabiah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4812686/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 Energy in all forms is a key requirement for human livelihoods and socio-economic development. However, overreliance on a single sthece of energy can cause energy management issues because of the occurrence of system over-burdening. Thus, the utilization of other forms of energy is highly promoted worldwide, with a clear emphasis on enhancing environmentalism and reducing pollution through waste management and control. This paper considers the use of digital image processing in the form of pattern recognition to extract the pattern of a coconut shell and charcoal wood to show the correlation between their patterns and thus deduce the energy properties of the coconut shell. A 3D camera is used to capture the digital image of the preprocessed coconut shell. The appropriate algorithm is then written on the MATLAB software toolbox to manipulate and translate the digital images, hence revealing its hidden nature. The technical process of scrutinizing the hidden properties involves the changes of pixels of the images, enhancement and thresholding; which is the pattern revealing step. Finally, the automatic pattern recognition toolbox acts to recognize the resemblance of the pattern of the coconut shells to the wood charcoal in order to analyze the pattern directly and determine the energy property percentage indices of the agent under test. The results indicate that coconut shells is carbon based, first- generation bioenergy crop and has high bioenergy properties and again a lower ignition property compared to charcoal wood. Renewable Resources Greening Waste Management Pollution Pyrolysis Bioenergy 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 Figure 24 Figure 25 Figure 26 Figure 27 1. INTRODUCTION Modern technological advancements and their benefits make it possible for most branches of research and technology to be affected by the use of image processing methods. It has a wide range of applications such as biomass analysis, medicine (X-rays, MRI, analysis of cell images, agriculture application via aerial views, inspection of fruits and vegetables, industry applications by automatic inspection of items, law enforcement via using fingerprint analysis and many more. All these applications show that image processing and analysis today provide a solid tool to solve many real-life problems. In the security field, digital image processing is used to analyse CCTV surveillance system by using various techniques (V. Jaiswal, 2018 ) to assure security, monitor road traffics and even track fraudsters and law enforcers. The medical fields have also been affected by the use digital image processing to reveal unseen diseases following pregnancy (G. Dougherty, 2009 ) and many more applications. Agriculture especially is one of the biggest sectors producing a massive amount of waste every year (E. T. Quartey, 2011 ) which affects the environment. But, this amount of biodegradable waste generated can be converted to a tremendous amount of energy. Perceived as the future of renewable energy stheces, biomass is promising since the composition of its structure plays an important role in the emission of heat for the transformation of biofuels (M. N. S.Sinha et al. 2015). Therefore, considering the numerous advantages such as limitation of greenhouse emissions, reduction of pollution, provision of bioenergy such as heat, electricity etc., and ultimately proper biomass management must be done in such a way that it profits humanity and nature (M. N. S.Sinha et al. 2015). This project will have a great environmental and also economic impact because energy production such as heat is mostly done by burning wood which causes deforestation. Whereas coconut shells; which is a mostly considered waste, possesses some energy properties and be more profitable when proven to be a good sthece of bioenergy. Digital image processing and analysis have been undergoing a vigorous growth as a subject of interdisciplinary study and has been applied for research in several fields for pattern recognition. Pattern recognition and image processing have similarities, the process of processing details of an image by improving its appearance and ensuring that it is properly represented, it does not only involve image coding, filtering, enhancement and restoration, but also feature extraction, analysis and recognition the image. Recently, image processing has largely been utilized for quantitative analysis of biomass and other biological systems such as bacteria and yeast. Biomass could be defined as a renewable natural fabric which comes from plants and animals and can be handled to be utilized as fuel or to create power. Digital image processing was applied to measure and quantify the biomass of an organic matter, this method is a simple and an efficient way to analyze the biomass content of an element and its properties needed for many purposes. This study focuses on the analysis of the energy properties of coconut shells by using a combination of digital image processing and pattern recognition. It was Established that the bioenergy properties of coconut shells will ultimately combat existing issues relating to the recycling of coconut shells. Thus, enhancing environmentalism and greening. 1.1 Problem Statement Currently, the recycling of coconut shells is a major problem in Ghana and coconut is produced on approximately 11.8 million hectares of land in 92 countries around the world (Ofori-Agyeman C. 2016). Ghana is ranked 16th in the production, producing 366.183 tons of coconut (2010) (Ofori-Agyeman C. 2016). Ghana generates tons of coconut shells annually, apart from a small percentage of the shells that are burned as fuel, the remainder is normally discarded. Coconut sellers dump coconut shells and shuck after close of business and this has contributed to pollution in Ghana. The environment cannot be protected if waste materials are not managed, a lot of people are not aware of the huge economic potential and uses of coconut shells. It’s important to prove that, coconut shells possess remarkable properties which can be an alternative energy sthece due to its several characteristics. Coconut shells should not be considered as a waste that degrades the environment but rather be considered as one with high potentials in the renewable energy and recycling industry. 1.2 Research Objectives The main Objective of this study is to analyze the energy properties of coconut shells by using Digital Image Processing and pattern recognition through MATLAB. Specific Objectives : To acquire digital images of coconut shell and identify its inherent patterns to facilitate the analysis of its energy properties To compare patterns of coconut shell and other related carbon-based biomass using thresholding To validate the analysis of energy properties of coconut shell 2. Review of Digital Image Analysis Digital image analysis is an area meant for establishing quantitative measurements to generate a description from an image. At a most advanced level image analysis is very crucial as it might be the center of an important decision making. Image analysis techniques may require the addition or the extraction of some elements to aid achieving the wanted goal. Here are the different techniques: Image classification ; its objective is to identify and extract information classes from a multiband image as a unique gray level or color. Image segmentation ; it is a technique enhance digital image analysis by partitioning a digital image into various subgroups of pixels in order to reduce the complexity of the image and make analysis simpler. Image particle analysis ; it refers to a technique mostly used to identify tiny particles and increase their sizes, and reveal their shape for the analysist to be able to extract his need. Thresholding ; almost similar to image segmentation, thresholding consists of changing the pixel of an image to make it easier to analyze. It is mostly a way to select areas of interest of an image while ignoring the rest of the image. Grayscale ; mostly used in photography, grayscale analysis is a technique in which the concentration of all colors present in a picture are equally sampled to get a gray fade. For example, it known most images bear bright colors such as red blue and green and all mixed together according to their pixel concentration form other colors. In this analysis, the pixels of these three colors are equally mixed to give a gray fade hence it makes easier to analyze the image. 2.1 Analysis of Biomass (Coconut Shell) Biomass is defined as a renewable organic material that comes whether from an animal or a floral sthece which can be used as a sthece of energy (bioenergy) such as fuel or electricity. Here, a case is placed on and emphasizes on coconut shell. Biomass contains various organic molecules such hydrogen, oxygen, alkaline and heavy metal depending on the sthece but stays a carbon-based element. Many processes are used to transform this material into an energy sthece, but the common ones are Photosynthesis ; it is a process by which green plants absorb light energy in their organisms and convert their various organic molecule into chemical energy. Pyrolysis ; it is a process in which organic materials are subject to a thermal decomposition at elevated temperature in an inert environment. 2.2 Coconut Shell and Energy properties In 2018, Yong Suk C. et al. conducted research into the estimation of sorghum biomass using digital image analysis with Canopeo where digital image is applied to analyze the correlation between the states of growth a sorghum plant and its production of biomass. Here, the research was conducted all through the evolution of the sorghum plant to demonstrate that there is a correlation between plant tallness and biomass generation. Congruently, coconut shell is the hardest part inside the husk of a coconut and protects the soft part of the fruit. Coconut has two states, it may be mature which means that it is composed of an average of 15% of shell or it may be dry with an average of 23% of shell (K. Ragavan, 2010). Figure 1 shows the compisostion of a maruured and dry coconut by weight. Coconut is produced in 92 countries around the globe and occupies about more than 10 million hectares (S. Zafar, 2021 ). Coconut shell is a great renewable biomass energy and its presence everywhere is being turned as a sthece of energy rather than a means of pollution. Coconut shells have a high calorific value of 20.8MJ/Kg (S. Zafar, 2021 ) and can be used to generate steam, energy-rich gases, bio-oil, biochar, electricity and heat. This biomass transformation into energy is done by pyrolysis. What is yet to be established is the ignition properties of coconut shell. Hence, the need to conduct more research with different approaches to further exploit the energy properties of coconut shell. In 2020, M.-H. S. Hashem Asgharnejad researched on ‘Development of Digital image processing as an innovative method for activated sludge biomass quantification’ shots of sludge biomass were taken with a camera (Nikon D5300, Japan) prepared with a focal point (Nikon, 18–140 mm f? 3.5–5.6 VR, Japan). The use of a consistent zoom, primordial induced a larger image. Image analysis was then conducted using RGB analysis techniques to equalize the pixel of all bright colors and get gray fade. This technique helped the researcher to focus on particles of sludge biomass needed for their studies. It was established that due to the fact that, applying digital image processing in the areas of biomass is new, it will be relevant in the field of biomass quantification. Again, E. T. A. Oolayo M. Ikumapayi in 2019, worked on the entire exocarp of coconut shell by evacuating the shell in order to be able to reach the shell containing the natural product. The shell known as endocarp was washed and sterilized to get it warmed to be dry. The shell was smashed into littler sizes and afterwards Pulverized into large scale particles and finally processed to guarantee having better molecule sizes. The composition of the Coconut Shell Nano Particles was carried out by the Vitality Dispersive X-ray Spectroscopy and also chemical compositions of coconut shell was deduced from the X-ray. Again, thresholding strategies was then utilized in this study to analyze the procured images of the coconut nanoparticles by changing the pixels of these images and selecting ideal edge esteem to decide the impact of processing time and digital image processing on the microstructure. 3. Methodology 3.1 Required Specification Digital image processing has proven of the attainment of accurate results when applied for pattern recognition and correlative purposes and assumption. Here, various steps are involved such as: Image acquisition, it’s actually where the work starts, it’s the most important step since no processing can be conducted before obtaining an image. Here, the internal image plane is illuminated as shown in Fig. 2 . Image enhancement which consists of processing the image while using different digital image processing methods to remove any defect that can affect the image as shown in Fig. 3 . Image segmentation, it’s the partition of the image into different parts called segments which is going to help identify all the objects present in the image. Figure 4 illustrate a segmented image. Thresholding which is a method used to convert image to binary image is shown in Fig. 5 , it involves the processing of white and black image out of a grayscale image by arranging those pixels to white whose value is above a given threshold. 3.2 Digital Image Processing Procedure The process applies a knowledge-based algorithm to acquire, restore, enhance, segment, compress and recognize an image. 3.2.2 Data Collection Requirement The data collection is the object we are using for the analysis. It was needed to get the extract of coconut shell which has been collected from a coconut shell dump in the city of Accra and also the wood from Afram plains Charcoal’s makers which is already a carbon neutral component and will play an important role through the study. 3.2.3 Hardware Requirements The hardware requirements represent the external components used to obtain all the images of coconut shell and wood, which has permitted the analysis. - IPhone 11 ,256gb,60fps, 12-megapixel (f/1.8) + 12-megapixel (f/2.4) + 12-megapixel (f/2.0) - Dell laptop, intel core i7 RAM 16gb 3.2.4 Software requirements MATLAB toolbox is the software used here, after acquiring the digital image of the object, it is upload it for the analysis. The process of image acquisition is to take a shot of the object which will give the 2D format needed for digital utilization and processing. Uploading images of the objects in the MATLAB software toolbox means that, certain variables need to be declared for the software for recognition. Variable’s declaration is done by using the syntaxes for the Coconut shell and for the wood in order to be identified by the computer. Then RGB to grayscale is applied to the digital images firstly to reduce the value of the image and eliminate all the red, green and blue sets of pixels. Achieving a gray image is important for image processing. The gray image is then enhanced for more clarity and also to improve the quality of the images. This operation highlights the patterns present on the Coconut and the wood after inputting the syntax. 4. Results Analysis Here, the data set represents the collection of elements that give rights to conduct the research. By trying to prove that coconut shell has energy properties and its carbon neutral, various external components have been used to get the extraction of some morphological features of coconut shell and wood, another main stage of this project is color infusing; which consists of the conversion of the image color from RGB to grayscale, which plays an important role in permitting the accusation of a clear and accurate overview of the object. It also reveals and identifies all the pattern presents, then via obtaining the object description, analysis of the data can be conducted. It is necessary to notice that every step was enshrined in the algorithm by a MATLAB language syntax. The following steps are the main processes used for the analysis: Variable Declaration This step consists of Renaming input data for the software to identify them and then be able to recognize them at any point of time when the data are needed for processing. Image display After declaring the variables (Images), which do not appear directly on the workspace. Another line of code is written for the image to be displayed and reassure the user of the effectiveness of the data. RGB to grayscale Efficiency in image processing attainment requires the image size to be reduced by converting the RGB images into grayscale. -Image enhancement Reducing the size of an image makes digital image processing easier but may also affect the quality of the image, this is why we enhance the gray image in order to eliminate all flaws and getting a much clear view of the working area. Thresholding Applying thresholding consist of segmenting a grayscale image by changing pixels intensity and applying threshold values. This leads to separating the image into foreground value and background value. This values represents numerous highlighted sections of the images to be analyzed. S = imread('IMG_5087.jpeg'); imshow (S) Sgray = rgb2gray (S); imshow (Sgray) Sh = histeq (Sgray); imshow (Sh) level = 0.5; Stresh = imbinarize (Sh,level); imshow (Stresh) W = imread ('IMG_3710.jpeg'); imshow (W) Wgray = rgb2gray (W); imshow (Wgray) Wtresh = imbinarize(Wh,level); imshow (Wtresh) One of the most important steps in the image analysis which is using the operated via the syntax < > for the coconut shell and the syntax < > for the wood is called thresholding which is meant to create a binary image of the enhanced gray pictures. Threshold values will be inputed and varied until the pixel intensity of the images are manipulated enough to separate the wanted regions from the unwanted ones. Then, the Neural network training tool is activated as a section in Matlab that performs Pattern recognition which is the last step in the experiment. level2 = 0.6; Wtresh2 = imbinarize (Wh,level2); imshow (Wtresh2) Wtresh3 = imbinarize(Wh,level3); imshow (Wtresh3) imshowpair(Stresh2,Wtresh3,'montage') imshowpair(Stresh3,Wtresh4,'montage') As the images have been inputted, the default of ‘Number of neurons’ is put into memory. This number is 10 to evaluate the threshold coconut shell image and later add ‘1’ to the default number of neurons to evaluate the threshold charcoal wood image. 4.1 Performance of Energy properties The following graphs represent the performance of energy properties collected right after the thresholding process. It shows the presence of other types of energy and the slight difference between coconut shell and charcoal wood. Pattern Recognition with Matlab is basically defined as the process whereby; a received pattern is assigned to one of a prescribed number of classes which is operated with the neural network tool. The syntax to have access to that tool is ‘nprtool’. Once the tool is accessed, the pattern recognition application is selected and then the ‘train’ button is activated to allow the software to evaluate the output of the written algorithm and apply pattern recognition. In the cases, two sets of patterns from a coconut shell and a charcoal revealed by an algorithm will be evaluated by the software and create different analysis matrixes that will analyse the patterns through machine learning system. The Different Matrixes in the case are: Training confusion matrix; which is a matrix meant to train or apply directly the applied method and different methods that can be used to analyse patterns. Testing confusion matrix; which is a matrix meant to test directly the applied method and different methods that can be used to analyse patterns. Validation confusion matrix; which is the matrix meant to analyse directly the performance of the energy properties in the objects. All confusion matrix; which is the final matrix displaying the result of the patterns recognition. 4.2.1 Threshold comparison Threshold comparison is one of the results displaying the difference between the Matrixes of the raw image and the processed one. In the case we have different values visible in the matrixes from the gray scale stage to the thresholding stage. 4.3 Results of Pattern Recognition The confusion Matrixes below are the results of the pattern recognition analysis conducted. The results in the green boxes are called the ‘True positives’ which in this case mean that, the method used to segment patterns for the image is the right one hence delivers the energy property index at the bottom right boxes. The pink boxes are called the ‘True negatives’ which indicates that the other methods directly applied were not appropriate to analyze the patterns. Here, the results of ‘99.4%’ of energy property index in the two confusion matrixes table is the proof that coconut shell is as carbon based as charcoal wood is and a good bioenergy agent. By using more than one method to conduct the analysis and writing the corresponding algorithms of coconut shell and wood as well abiding by the policies and step involved in this project, provides a solid backing of the research conducted. The positive results obtained in both cases establishes a convincing assessment of the bioenergy properties of coconut shell as expected and so a pronouncement of coconut shell being a sthece of energy is duly made. 4.4 A Proposed Framework of Potentials of Bioenergy (Coconut Shell) For Sustainability Generally, bioenergy plants increase soil carbon and fix atmospheric carbon for plant growth, making it essential in earth’s carbon cycle. Similarly, Biomass provide hydrogen, which can be chemically extracted to produce green hydrogen fuels for many benefits. Establishing the bioenergy chrematistics of coconut shell, possesses a great potential for the application of benefits of this bioenergy fuels for energy sustainability in sub- Saharan Africa. Figure 27 illustrates a proposed framework of the potentials of coconut shell. 3. CONCLUSION C02 released during the combusting of biomass is needed for plants growth. Congruently, in the usage of crop biomass for energy generation, no net CO2 is generated as the amount emitted during use has previously been utilized during plant growth. Essentially, the use of bioenergy crops for the production of energy enables the utilizing of an alternate sources of renewable energy. Digital image processing has an important role in human being life, with the help of digital image processing we are able to interpret, identify and analyze an image, so many applications shows that image processing is part of human life and has been solving issues. Many different reviews have revealed how digital image processing has been used to conduct many experiments and studies, although each study was done separately and has its own characteristic and limitation, this work has further showed the effectiveness of image processing and its problem-solving abilities. The main problem of the project work was to prove that coconut shell has bioenergy properties to control environmental pollution cause by considering coconut shell as waste and rather promote recycling and usage of renewable energy (Abubakar, R. 2020). By proving that the pattern of coconut shell is similar to wood pattern we ended up confirming that of its energy properties. Ultimately, the research target has been achieved. References V. Jaiswal,(2018) "Comparative Analysisi of CCTV video image processing Techniques and Application," IOSR Jthenal of Engineering (IOSRJEN), vol. 8, no. 10, pp. 2278-8719, 2018. G. DOUGHERTY, (2009)Digital image processing for medical applications, Channel islands: Cambridge University press, 2009. E. T. Quartey, (2011) "Briquetting Agricultural waste as an energy sthece in Ghana," in Recent research in environment, energy planning and pollution , Brno, 2011. M. said, (2015) "The study of kinetic properties and analytical pyrolysis of coconu shells," Jthenal of Renewable Energy, vol. 2015, 2015. D. P. Lakshmi Devi et al., (2020) "DIGITAL IMAGE PROCESSING (R15A0426)," Hyderabad, 2019-2020. A. K. jAIN, (1989) Fundamentals of digital image processing, Englewood: Prentice Hall, 1989. K. Ragavan, (2021) "Biofuels from coconut," Energypedia.info, Kanpur, 2010. S. Zafar, (2021)"Energy potential of coconut biomass," Bioenergy consult, 19 march 2021. K. Yong Suk Chung et al. (2018), "Estimation of Sorghum Biomass using digital image Analysis with canopeo," Biomass and abaioenergy, 2018. M.-H. S. Hashem Asgharnejad, (2020) "Development oDevelopment of digital image processing as an innovative method for activated sludge biomass quantification," Frontiers in microbiology, 2020. E. T. A. Oolayo M. Ikumapayi, (2019) "IMAGE Processing And Particle Size Analysis Of Coconut Shell Nanoparticles," International Jthenal of Engineering and Technology(IJCIET), vol. 10, pp. 2475-2482, 2 february 2019. D. B. Jähne, (1995), Digital Image processing (Concepts, Algorithms and Scientific applications), 3rd ed., San Diego: Springer-Verlag Berlin Heidelberg New York, 1995. A. McAndrew (2004). “ An introduction to digital image processing with MATLAB.Notes for Scm2511 Image Processing 1,Semester 1, Victoria University Of Technology: School of computer science and mathematics, 2004. A. S.Sinha (2018), "Regression based integrated bi-sensor Sar Data Model to estimate forest carbon stock," Jthenal Of Forestry Research , 2018. M. N. S.Sinha et al. (2015), "A review of radar remote sensing for biomass estimation," International Jthenal of Environmental science and technology , 2015. Julian D Colorado et al. (2020), "A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops," PLOS ONE , 2020. M. J. K. D. Monika Mierzwa‑Hersztek1 et al. (2019), "Assessment of energy parameters of biomass and biochars," Jthenal of Material Cycles and Waste Management, pp. 786-800, 2019. Naana Tetebea Oyirifi, Ruhiya Abubakar, Emmanuel Kwame Effah et al. “Embracing Environmentalism By Assessing The Sustainability and Adoption Of Biomass As Boiler Fuel In The Manufacturing Industry”, 09 July 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3972339/v1] S. Zafar (2021), "Energy potential of coconut biomass," Bioenergy consult, 2021. V. K. Mishra, S. Kumar, and N. Shukla (2017), “Image Acquisition and Techniques to Perform Image Acquisition,”SAMRIDDHI A J. Phys. Sci. Eng. Technol., vol. 9, no. 01, pp. 21–24, 2017 Bhardwaj, N., Kaur, G., and Singh, P.K., “A systematic review on image enhancement techniques”,Sensors and Image Processing, Springer, Singapore, 227–235, (2018) Gandhi, M., Kamdar, J. & Shah, M. Preprocessing of non-symmetrical images for edge detection. Augment. Hum. Res. 5, 1–10(2020) Abubakar, R., Kumar, K.S., Acakpovi, A., Ayinga, U.W., Prempeh, N.A., Tetteh, J. and Kumassah, E.S., 2020, December. Convolutional neural networks for solid waste segregation and prospects of waste-to-energy in ghana. In Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management Harare, Zimbabwe Additional Declarations The authors declare no competing interests. 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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-4812686","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332502773,"identity":"e1d23f7c-b6c3-45ee-b0ca-d7dac51ea6d2","order_by":0,"name":"Ruhiya Abubakar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACHgZmBoYEBgb2BqjAAQaQCDFaeA6AVZOiRSKBSC3m7WePSRfUpMmbz3x+8fPHNgY5vhsJzK8L8GiROZOXJj3jWI7hnNs5xRIH2xiMJW8ksFnPwKNFgiHHTJqHrYJxhnROGgNQS+IGoBZjHnxa+N8AtfyrsJ8heQaspZ6wFgmgLbxtOYkzJNiPgbQkGAD98hi/lnfJ1jP70pJn8OQwS5w5J2E488zDNmb8Dss9eLvgW7LtDPbjDz9UlNnI8x1PPvwZnxYkwGMAMgKIGdskiNPBwP4AxmL+QKSWUTAKRsEoGBkAALFkS+kUBWRUAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3978-7598","institution":"Ghana Communicaton Technology University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ruhiya","middleName":"","lastName":"Abubakar","suffix":""},{"id":332502774,"identity":"da66038b-7131-40d7-8241-71ebd82d1373","order_by":1,"name":"N’da Comoe Axel Aymeric","email":"","orcid":"https://orcid.org/0009-0006-9661-1392","institution":"Ghana Communication Technology University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"N’da","middleName":"Comoe Axel","lastName":"Aymeric","suffix":""},{"id":332502775,"identity":"0c1921bb-9e32-4f10-b425-7ed1ef867ecb","order_by":2,"name":"Amevi Acakpovi","email":"","orcid":"https://orcid.org/0000-0003-1838-0155","institution":"Accra Tecnical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amevi","middleName":"","lastName":"Acakpovi","suffix":""},{"id":332502776,"identity":"df8eb8a4-16e1-4f7b-bed9-793cac410d9a","order_by":3,"name":"Solomon Nsor Anabiah","email":"","orcid":"https://orcid.org/0000-0002-5687-2057","institution":"Ghana Communicaton Technology University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Solomon","middleName":"Nsor","lastName":"Anabiah","suffix":""}],"badges":[],"createdAt":"2024-07-27 10:55:00","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-4812686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4812686/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61809342,"identity":"8d42ba25-c0dc-4da9-8574-7130a3d5e8a5","added_by":"auto","created_at":"2024-08-05 20:14:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompositions of Mature and Dry Coconut by Weight (K. 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4","display":"","copyAsset":false,"role":"figure","size":223592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample of image segmentation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ehttps://www.mathworks.com/discovery/image-segmentation.html\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/313f373cca02cbf08254bf71.png"},{"id":61809344,"identity":"c23f3ce2-4139-46c1-ac9c-28ab0c2638a4","added_by":"auto","created_at":"2024-08-05 20:14:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample of using thresholding to convert an 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7","display":"","copyAsset":false,"role":"figure","size":50741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFundamental Block Diagram of Image Processing\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/5f62949b0a0bad85856d9a55.png"},{"id":61811784,"identity":"e01d8b47-cb34-46fd-842f-273139acf18b","added_by":"auto","created_at":"2024-08-05 20:30:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":372416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample of pattern recognition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/e10746a9d06aaf020e4bdce7.png"},{"id":61809348,"identity":"775e6399-ea31-44a8-835d-b6699043487e","added_by":"auto","created_at":"2024-08-05 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19","display":"","copyAsset":false,"role":"figure","size":177916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFtheth level coconut shell Thresholding and Ftheth level Charcoal wood Thresholding\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/a1ac97493fcc53f90807f3bf.png"},{"id":61810905,"identity":"8dcfb63a-a54c-41d4-8bec-9aa09586421f","added_by":"auto","created_at":"2024-08-05 20:22:58","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":162180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCoconut Shell Energy properties performance 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Thresholding Matrix\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"24.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/608f9aed46a36ac7f3e50416.png"},{"id":61809366,"identity":"487b8cca-2d02-468c-8427-1d702144773a","added_by":"auto","created_at":"2024-08-05 20:14:58","extension":"png","order_by":25,"title":"Figure 25","display":"","copyAsset":false,"role":"figure","size":87039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharcoal Wood Thresholding Matrix\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"25.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/da7ec4ae8c8616146eb49ae8.png"},{"id":61809364,"identity":"50f89ac4-0703-45c9-a1f2-aaa7c9f766b4","added_by":"auto","created_at":"2024-08-05 20:14:58","extension":"png","order_by":26,"title":"Figure 26","display":"","copyAsset":false,"role":"figure","size":441441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCoconut shell Confusion Matrixes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharcoal wood confusion matrixes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"26.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/d3d84ec71bf58bb8024cc311.png"},{"id":61809359,"identity":"266784fa-a003-427b-9c54-b73dca446fb4","added_by":"auto","created_at":"2024-08-05 20:14:57","extension":"png","order_by":27,"title":"Figure 27","display":"","copyAsset":false,"role":"figure","size":119845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed Framework of the\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"27.png","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/2de2e32da7633a97db29742a.png"},{"id":61812448,"identity":"54e4ed58-e93b-4585-9333-4916e3ac4d0e","added_by":"auto","created_at":"2024-08-05 20:47:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6015477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4812686/v1/e48c2907-a9ec-4e3f-8996-cb7112c08c15.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEspousing Environmental Pollution Management and Control by Exploring the Bioenergy Properties of Coconut Shell Nanoparticles\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eModern technological advancements and their benefits make it possible for most branches of research and technology to be affected by the use of image processing methods. It has a wide range of applications such as biomass analysis, medicine (X-rays, MRI, analysis of cell images, agriculture application via aerial views, inspection of fruits and vegetables, industry applications by automatic inspection of items, law enforcement via using fingerprint analysis and many more. All these applications show that image processing and analysis today provide a solid tool to solve many real-life problems. In the security field, digital image processing is used to analyse CCTV surveillance system by using various techniques (V. Jaiswal, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to assure security, monitor road traffics and even track fraudsters and law enforcers. The medical fields have also been affected by the use digital image processing to reveal unseen diseases following pregnancy (G. Dougherty, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and many more applications.\u003c/p\u003e \u003cp\u003eAgriculture especially is one of the biggest sectors producing a massive amount of waste every year (E. T. Quartey, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) which affects the environment. But, this amount of biodegradable waste generated can be converted to a tremendous amount of energy. Perceived as the future of renewable energy stheces, biomass is promising since the composition of its structure plays an important role in the emission of heat for the transformation of biofuels (M. N. S.Sinha et al. 2015).\u003c/p\u003e \u003cp\u003eTherefore, considering the numerous advantages such as limitation of greenhouse emissions, reduction of pollution, provision of bioenergy such as heat, electricity etc., and ultimately proper biomass management must be done in such a way that it profits humanity and nature (M. N. S.Sinha et al. 2015).\u003c/p\u003e \u003cp\u003eThis project will have a great environmental and also economic impact because energy production such as heat is mostly done by burning wood which causes deforestation. Whereas coconut shells; which is a mostly considered waste, possesses some energy properties and be more profitable when proven to be a good sthece of bioenergy.\u003c/p\u003e \u003cp\u003eDigital image processing and analysis have been undergoing a vigorous growth as a subject of interdisciplinary study and has been applied for research in several fields for pattern recognition. Pattern recognition and image processing have similarities, the process of processing details of an image by improving its appearance and ensuring that it is properly represented, it does not only involve image coding, filtering, enhancement and restoration, but also feature extraction, analysis and recognition the image. Recently, image processing has largely been utilized for quantitative analysis of biomass and other biological systems such as bacteria and yeast. Biomass could be defined as a renewable natural fabric which comes from plants and animals and can be handled to be utilized as fuel or to create power. Digital image processing was applied to measure and quantify the biomass of an organic matter, this method is a simple and an efficient way to analyze the biomass content of an element and its properties needed for many purposes. This study focuses on the analysis of the energy properties of coconut shells by using a combination of digital image processing and pattern recognition. It was Established that the bioenergy properties of coconut shells will ultimately combat existing issues relating to the recycling of coconut shells. Thus, enhancing environmentalism and greening.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Problem Statement\u003c/h2\u003e \u003cp\u003eCurrently, the recycling of coconut shells is a major problem in Ghana and coconut is produced on approximately 11.8\u0026nbsp;million hectares of land in 92 countries around the world (Ofori-Agyeman C. 2016). Ghana is ranked 16th in the production, producing 366.183 tons of coconut (2010) (Ofori-Agyeman C. 2016).\u003c/p\u003e \u003cp\u003eGhana generates tons of coconut shells annually, apart from a small percentage of the shells that are burned as fuel, the remainder is normally discarded. Coconut sellers dump coconut shells and shuck after close of business and this has contributed to pollution in Ghana. The environment cannot be protected if waste materials are not managed, a lot of people are not aware of the huge economic potential and uses of coconut shells. It\u0026rsquo;s important to prove that, coconut shells possess remarkable properties which can be an alternative energy sthece due to its several characteristics. Coconut shells should not be considered as a waste that degrades the environment but rather be considered as one with high potentials in the renewable energy and recycling industry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Research Objectives\u003c/h2\u003e \u003cp\u003eThe main Objective of this study is to analyze the energy properties of coconut shells by using Digital Image Processing and pattern recognition through MATLAB.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecific Objectives\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo acquire digital images of coconut shell and identify its inherent patterns to facilitate the analysis of its energy properties\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo compare patterns of coconut shell and other related carbon-based biomass using thresholding\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo validate the analysis of energy properties of coconut shell\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Review of Digital Image Analysis","content":"\u003cp\u003eDigital image analysis is an area meant for establishing quantitative measurements to generate a description from an image. At a most advanced level image analysis is very crucial as it might be the center of an important decision making. Image analysis techniques may require the addition or the extraction of some elements to aid achieving the wanted goal. Here are the different techniques:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImage classification\u003c/b\u003e; its objective is to identify and extract information classes from a multiband image as a unique gray level or color.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImage segmentation\u003c/b\u003e; it is a technique enhance digital image analysis by partitioning a digital image into various subgroups of pixels in order to reduce the complexity of the image and make analysis simpler.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImage particle analysis\u003c/b\u003e; it refers to a technique mostly used to identify tiny particles and increase their sizes, and reveal their shape for the analysist to be able to extract his need.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThresholding\u003c/b\u003e; almost similar to image segmentation, thresholding consists of changing the pixel of an image to make it easier to analyze. It is mostly a way to select areas of interest of an image while ignoring the rest of the image.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGrayscale\u003c/b\u003e; mostly used in photography, grayscale analysis is a technique in which the concentration of all colors present in a picture are equally sampled to get a gray fade. For example, it known most images bear bright colors such as red blue and green and all mixed together according to their pixel concentration form other colors. In this analysis, the pixels of these three colors are equally mixed to give a gray fade hence it makes easier to analyze the image.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Analysis of Biomass (Coconut Shell)\u003c/h2\u003e \u003cp\u003eBiomass is defined as a renewable organic material that comes whether from an animal or a floral sthece which can be used as a sthece of energy (bioenergy) such as fuel or electricity. Here, a case is placed on and emphasizes on coconut shell.\u003c/p\u003e \u003cp\u003eBiomass contains various organic molecules such hydrogen, oxygen, alkaline and heavy metal depending on the sthece but stays a carbon-based element. Many processes are used to transform this material into an energy sthece, but the common ones are\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePhotosynthesis\u003c/b\u003e; it is a process by which green plants absorb light energy in their organisms and convert their various organic molecule into chemical energy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePyrolysis\u003c/b\u003e; it is a process in which organic materials are subject to a thermal decomposition at elevated temperature in an inert environment.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Coconut Shell and Energy properties\u003c/h2\u003e \u003cp\u003eIn 2018, Yong Suk C. et al. conducted research into the estimation of sorghum biomass using digital image analysis with Canopeo where digital image is applied to analyze the correlation between the states of growth a sorghum plant and its production of biomass. Here, the research was conducted all through the evolution of the sorghum plant to demonstrate that there is a correlation between plant tallness and biomass generation.\u003c/p\u003e \u003cp\u003eCongruently, coconut shell is the hardest part inside the husk of a coconut and protects the soft part of the fruit. Coconut has two states, it may be mature which means that it is composed of an average of 15% of shell or it may be dry with an average of 23% of shell (K. Ragavan, 2010). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the compisostion of a maruured and dry coconut by weight.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCoconut is produced in 92 countries around the globe and occupies about more than 10\u0026nbsp;million hectares (S. Zafar, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoconut shell is a great renewable biomass energy and its presence everywhere is being turned as a sthece of energy rather than a means of pollution.\u003c/p\u003e \u003cp\u003eCoconut shells have a high calorific value of 20.8MJ/Kg (S. Zafar, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and can be used to generate steam, energy-rich gases, bio-oil, biochar, electricity and heat. This biomass transformation into energy is done by pyrolysis. What is yet to be established is the ignition properties of coconut shell. Hence, the need to conduct more research with different approaches to further exploit the energy properties of coconut shell.\u003c/p\u003e \u003cp\u003eIn 2020, M.-H. S. Hashem Asgharnejad researched on \u0026lsquo;Development of Digital image processing as an innovative method for activated sludge biomass quantification\u0026rsquo; shots of sludge biomass were taken with a camera (Nikon D5300, Japan) prepared with a focal point (Nikon, 18\u0026ndash;140 mm f? 3.5\u0026ndash;5.6 VR, Japan). The use of a consistent zoom, primordial induced a larger image. Image analysis was then conducted using RGB analysis techniques to equalize the pixel of all bright colors and get gray fade. This technique helped the researcher to focus on particles of sludge biomass needed for their studies. It was established that due to the fact that, applying digital image processing in the areas of biomass is new, it will be relevant in the field of biomass quantification. Again, E. T. A. Oolayo M. Ikumapayi in 2019, worked on the entire exocarp of coconut shell by evacuating the shell in order to be able to reach the shell containing the natural product. The shell known as endocarp was washed and sterilized to get it warmed to be dry. The shell was smashed into littler sizes and afterwards\u003c/p\u003e \u003cp\u003ePulverized into large scale particles and finally\u003c/p\u003e \u003cp\u003eprocessed to guarantee having better molecule sizes.\u003c/p\u003e \u003cp\u003eThe composition of the Coconut Shell Nano Particles was carried out by the Vitality Dispersive X-ray Spectroscopy and also chemical compositions of coconut shell was deduced from the X-ray. Again, thresholding strategies was then utilized in this study to analyze the procured images of the coconut nanoparticles by changing the pixels of these images and selecting ideal edge esteem\u003c/p\u003e \u003cp\u003eto decide the impact of processing time and digital image processing on the microstructure.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Required Specification\u003c/h2\u003e\n \u003cp\u003eDigital image processing has proven of the attainment of accurate results when applied for pattern recognition and correlative purposes and assumption. Here, various steps are involved such as:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eImage acquisition, it\u0026rsquo;s actually where the work starts, it\u0026rsquo;s the most important step since no processing can be conducted before obtaining an image.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eHere, the internal image plane is illuminated as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eImage enhancement which consists of processing the image while using different digital image processing methods to remove any defect that can affect the image as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eImage segmentation, it\u0026rsquo;s the partition of the image into different parts called segments which is going to help identify all the objects present in the image. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate a segmented image.\u003c/p\u003e\n \u003cp\u003eThresholding which is a method used to convert image to binary image is shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, it involves the processing of white and black image out of a grayscale image by arranging those pixels to white whose value is above a given threshold.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Digital Image Processing Procedure\u003c/h2\u003e\n \u003cp\u003eThe process applies a knowledge-based algorithm to acquire, restore, enhance, segment, compress and recognize an image.\u003c/p\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Data Collection Requirement\u003c/h2\u003e\n \u003cp\u003eThe data collection is the object we are using for the analysis. It was needed to get the extract of coconut shell which has been collected from a coconut shell dump in the city of Accra and also the wood from Afram plains Charcoal\u0026rsquo;s makers which is already a carbon neutral component and will play an important role through the study.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Hardware Requirements\u003c/h2\u003e\n \u003cp\u003eThe hardware requirements represent the external components used to obtain all the images of coconut shell and wood, which has permitted the analysis.\u003c/p\u003e\n \u003cp\u003e- IPhone 11 ,256gb,60fps, 12-megapixel (f/1.8)\u0026thinsp;+\u0026thinsp;12-megapixel (f/2.4)\u0026thinsp;+\u0026thinsp;12-megapixel (f/2.0)\u003c/p\u003e\n \u003cp\u003e- Dell laptop, intel core i7 RAM 16gb\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.4 Software requirements\u003c/h2\u003e\n \u003cp\u003eMATLAB toolbox is the software used here, after acquiring the digital image of the object, it is upload it for the analysis.\u003c/p\u003e\n \u003cp\u003eThe process of image acquisition is to take a shot of the object which will give the 2D format needed for digital utilization and processing.\u003c/p\u003e\n \u003cp\u003eUploading images of the objects in the MATLAB software toolbox means that, certain variables need to be declared for the software for recognition. Variable\u0026rsquo;s declaration is done by using the syntaxes for the Coconut shell and for the wood in order to be identified by the computer. Then RGB to grayscale is applied to the digital images firstly to reduce the value of the image and eliminate all the red, green and blue sets of pixels. Achieving a gray image is important for image processing. The gray image is then enhanced for more clarity and also to improve the quality of the images. This operation highlights the patterns present on the Coconut and the wood after inputting the syntax.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results Analysis","content":"\u003cp\u003eHere, the data set represents the collection of elements that give rights to conduct the research. By trying to prove that coconut shell has energy properties and its carbon neutral, various external components have been used to get the extraction of some morphological features of coconut shell and wood, another main stage of this project is color infusing; which consists of the conversion of the image color from RGB to grayscale, which plays an important role in permitting the accusation of a clear and accurate overview of the object. It also reveals and identifies all the pattern presents, then via obtaining the object description, analysis of the data can be conducted.\u003c/p\u003e\n\u003cp\u003eIt is necessary to notice that every step was enshrined in the algorithm by a MATLAB language syntax. The following steps are the main processes used for the analysis:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eVariable Declaration\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis step consists of Renaming input data for the software to identify them and then be able to recognize them at any point of time when the data are needed for processing.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eImage display\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAfter declaring the variables (Images), which do not appear directly on the workspace. Another line of code is written for the image to be displayed and reassure the user of the effectiveness of the data.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eRGB to grayscale\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEfficiency in image processing attainment requires the image size to be reduced by converting the RGB images into grayscale.\u003c/p\u003e\n\u003cp\u003e-Image enhancement\u003c/p\u003e\n\u003cp\u003eReducing the size of an image makes digital image processing easier but may also affect the quality of the image, this is why we enhance the gray image in order to eliminate all flaws and getting a much clear view of the working area.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThresholding\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eApplying thresholding consist of segmenting a grayscale image by changing pixels intensity and applying threshold values. This leads to separating the image into foreground value and background value. This values represents numerous highlighted sections of the images to be analyzed.\u003c/p\u003e\n\u003cp\u003eS\u0026thinsp;=\u0026thinsp;imread(\u0026apos;IMG_5087.jpeg\u0026apos;);\u003c/p\u003e\n\u003cp\u003eimshow (S)\u003c/p\u003e\n\u003cp\u003eSgray\u0026thinsp;=\u0026thinsp;rgb2gray (S);\u003c/p\u003e\n\u003cp\u003eimshow (Sgray)\u003c/p\u003e\n\u003cp\u003eSh\u0026thinsp;=\u0026thinsp;histeq (Sgray);\u003c/p\u003e\n\u003cp\u003eimshow (Sh)\u003c/p\u003e\n\u003cp\u003elevel\u0026thinsp;=\u0026thinsp;0.5;\u003c/p\u003e\n\u003cp\u003eStresh\u0026thinsp;=\u0026thinsp;imbinarize (Sh,level);\u003c/p\u003e\n\u003cp\u003eimshow (Stresh)\u003c/p\u003e\n\u003cp\u003eW\u0026thinsp;=\u0026thinsp;imread (\u0026apos;IMG_3710.jpeg\u0026apos;);\u003c/p\u003e\n\u003cp\u003eimshow (W)\u003c/p\u003e\n\u003cp\u003eWgray\u0026thinsp;=\u0026thinsp;rgb2gray (W);\u003c/p\u003e\n\u003cp\u003eimshow (Wgray)\u003c/p\u003e\n\u003cp\u003eWtresh\u0026thinsp;=\u0026thinsp;imbinarize(Wh,level);\u003c/p\u003e\n\u003cp\u003eimshow (Wtresh)\u003c/p\u003e\n\u003cp\u003eOne of the most important steps in the image analysis which is using the operated via the syntax\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;Stresh\u0026thinsp;=\u0026thinsp;imbinarize (Sh,level);\u0026gt;\u0026gt; for the coconut shell and the syntax\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;Wtresh\u0026thinsp;=\u0026thinsp;imbinarize(Wh,level);\u0026gt;\u0026gt; for the wood is called thresholding which is meant to create a binary image of the enhanced gray pictures. Threshold values will be inputed and varied until the pixel intensity of the images are manipulated enough to separate the wanted regions from the unwanted ones. Then, the Neural network training tool is activated as a section in Matlab that performs Pattern recognition which is the last step in the experiment.\u003c/p\u003e\n\u003cp\u003elevel2\u0026thinsp;=\u0026thinsp;0.6;\u003c/p\u003e\n\u003cp\u003eWtresh2\u0026thinsp;=\u0026thinsp;imbinarize (Wh,level2);\u003c/p\u003e\n\u003cp\u003eimshow (Wtresh2)\u003c/p\u003e\n\u003cp\u003eWtresh3\u0026thinsp;=\u0026thinsp;imbinarize(Wh,level3);\u003c/p\u003e\n\u003cp\u003eimshow (Wtresh3)\u003c/p\u003e\n\u003cp\u003eimshowpair(Stresh2,Wtresh3,\u0026apos;montage\u0026apos;)\u003c/p\u003e\n\u003cp\u003eimshowpair(Stresh3,Wtresh4,\u0026apos;montage\u0026apos;)\u003c/p\u003e\n\u003cp\u003eAs the images have been inputted, the default of \u0026lsquo;Number of neurons\u0026rsquo; is put into memory. This number is 10 to evaluate the threshold coconut shell image and later add \u0026lsquo;1\u0026rsquo; to the default number of neurons to evaluate the threshold charcoal wood image.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Performance of Energy properties\u003c/h2\u003e\n \u003cp\u003eThe following graphs represent the performance of energy properties collected right after the thresholding process. It shows the presence of other types of energy and the slight difference between coconut shell and charcoal wood.\u003c/p\u003e\n \u003cp\u003ePattern Recognition with Matlab is basically defined as the process whereby; a received pattern is assigned to one of a prescribed number of classes which is operated with the neural network tool. The syntax to have access to that tool is \u0026lsquo;nprtool\u0026rsquo;.\u003c/p\u003e\n \u003cp\u003eOnce the tool is accessed, the pattern recognition application is selected and then the \u0026lsquo;train\u0026rsquo; button is activated to allow the software to evaluate the output of the written algorithm and apply pattern recognition.\u003c/p\u003e\n \u003cp\u003eIn the cases, two sets of patterns from a coconut shell and a charcoal revealed by an algorithm will be evaluated by the software and create different analysis matrixes that will analyse the patterns through machine learning system. The Different Matrixes in the case are:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eTraining confusion matrix; which is a matrix meant to train or apply directly the applied method and different methods that can be used to analyse patterns.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTesting confusion matrix; which is a matrix meant to test directly the applied method and different methods that can be used to analyse patterns.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eValidation confusion matrix; which is the matrix meant to analyse directly the performance of the energy properties in the objects.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAll confusion matrix; which is the final matrix displaying the result of the patterns recognition.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Threshold comparison\u003c/h2\u003e\n \u003cp\u003eThreshold comparison is one of the results displaying the difference between the Matrixes of the raw image and the processed one. In the case we have different values visible in the matrixes from the gray scale stage to the thresholding stage.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Results of Pattern Recognition\u003c/h2\u003e\n \u003cp\u003eThe confusion Matrixes below are the results of the pattern recognition analysis conducted. The results in the green boxes are called the \u0026lsquo;True positives\u0026rsquo; which in this case mean that, the method used to segment patterns for the image is the right one hence delivers the energy property index at the bottom right boxes. The pink boxes are called the \u0026lsquo;True negatives\u0026rsquo; which indicates that the other methods directly applied were not appropriate to analyze the patterns. Here, the results of \u0026lsquo;99.4%\u0026rsquo; of energy property index in the two confusion matrixes table is the proof that coconut shell is as carbon based as charcoal wood is and a good bioenergy agent.\u003c/p\u003e\n \u003cp\u003eBy using more than one method to conduct the analysis and writing the corresponding algorithms of coconut shell and wood as well abiding by the policies and step involved in this project, provides a solid backing of the research conducted. The positive results obtained in both cases establishes a convincing assessment of the bioenergy properties of coconut shell as expected and so a pronouncement of coconut shell being a sthece of energy is duly made.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 \u003cstrong\u003eA Proposed Framework of Potentials of Bioenergy (Coconut Shell) For Sustainability\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eGenerally, bioenergy plants increase soil carbon and fix atmospheric carbon for plant growth, making it essential in earth\u0026rsquo;s carbon cycle. Similarly, Biomass provide hydrogen, which can be chemically extracted to produce green hydrogen fuels for many benefits. Establishing the bioenergy chrematistics of coconut shell, possesses a great potential for the application of benefits of this bioenergy fuels for energy sustainability in sub- Saharan Africa. Figure \u003cspan class=\"InternalRef\"\u003e27\u003c/span\u003e illustrates a proposed framework of the potentials of coconut shell.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. CONCLUSION","content":"\u003cp\u003eC02 released during the combusting of biomass is needed for plants growth. Congruently, in the usage of crop biomass for energy generation, no net CO2 is generated as the amount emitted during use has previously been utilized during plant growth. Essentially, the use of bioenergy crops for the production of energy enables the utilizing of an alternate sources of renewable energy.\u003c/p\u003e \u003cp\u003eDigital image processing has an important role in human being life, with the help of digital image processing we are able to interpret, identify and analyze an image, so many applications shows that image processing is part of human life and has been solving issues. Many different reviews have revealed how digital image processing has been used to conduct many experiments and studies, although each study was done separately and has its own characteristic and limitation, this work has further showed the effectiveness of image processing and its problem-solving abilities.\u003c/p\u003e \u003cp\u003eThe main problem of the project work was to prove that coconut shell has bioenergy properties to control environmental pollution cause by considering coconut shell as waste and rather promote recycling and usage of renewable energy (Abubakar, R. 2020). By proving that the pattern of coconut shell is similar to wood pattern we ended up confirming that of its energy properties. Ultimately, the research target has been achieved.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eV. Jaiswal,(2018) \u0026quot;Comparative Analysisi of CCTV video image processing Techniques and Application,\u0026quot; \u003cem\u003eIOSR Jthenal of Engineering (IOSRJEN),\u0026nbsp;\u003c/em\u003evol. 8, no. 10, pp. 2278-8719, 2018.\u003c/li\u003e\n \u003cli\u003eG. DOUGHERTY, (2009)Digital image processing for medical applications, Channel islands: Cambridge University press, 2009.\u003c/li\u003e\n \u003cli\u003eE. T. Quartey, (2011) \u0026quot;Briquetting Agricultural waste as an energy sthece in Ghana,\u0026quot; in \u003cem\u003eRecent research in environment, energy planning and pollution\u003c/em\u003e, Brno, 2011.\u003c/li\u003e\n \u003cli\u003eM. said, (2015) \u0026quot;The study of kinetic properties and analytical pyrolysis of coconu shells,\u0026quot; \u003cem\u003eJthenal of Renewable Energy,\u0026nbsp;\u003c/em\u003evol. 2015, 2015.\u003c/li\u003e\n \u003cli\u003eD. P. Lakshmi Devi et al., (2020) \u0026quot;DIGITAL IMAGE PROCESSING (R15A0426),\u0026quot; Hyderabad, 2019-2020.\u003c/li\u003e\n \u003cli\u003eA. K. jAIN, (1989) Fundamentals of digital image processing, Englewood: Prentice Hall, 1989.\u003c/li\u003e\n \u003cli\u003eK. Ragavan, (2021) \u0026quot;Biofuels from coconut,\u0026quot; Energypedia.info, Kanpur, 2010.\u003c/li\u003e\n \u003cli\u003eS. Zafar, (2021)\u0026quot;Energy potential of coconut biomass,\u0026quot; \u003cem\u003eBioenergy consult,\u0026nbsp;\u003c/em\u003e19 march 2021.\u003c/li\u003e\n \u003cli\u003eK. Yong Suk Chung et al. (2018), \u0026quot;Estimation of Sorghum Biomass using digital image Analysis with canopeo,\u0026quot; \u003cem\u003eBiomass and abaioenergy,\u0026nbsp;\u003c/em\u003e2018.\u003c/li\u003e\n \u003cli\u003eM.-H. S. Hashem Asgharnejad, (2020) \u0026quot;Development oDevelopment of digital image processing as an innovative method for activated sludge biomass quantification,\u0026quot; \u003cem\u003eFrontiers in microbiology,\u0026nbsp;\u003c/em\u003e2020.\u003c/li\u003e\n \u003cli\u003eE. T. A. Oolayo M. Ikumapayi, (2019) \u0026quot;IMAGE Processing And Particle Size Analysis Of Coconut Shell Nanoparticles,\u0026quot; \u003cem\u003eInternational Jthenal of Engineering and Technology(IJCIET),\u0026nbsp;\u003c/em\u003evol. 10, pp. 2475-2482, 2 february 2019.\u003c/li\u003e\n \u003cli\u003eD. B. J\u0026auml;hne, (1995), Digital Image processing (Concepts, Algorithms and Scientific applications), 3rd ed., San Diego: Springer-Verlag Berlin Heidelberg New York, 1995.\u003c/li\u003e\n \u003cli\u003eA. McAndrew (2004). \u0026ldquo; An introduction to digital image processing with MATLAB.Notes for Scm2511 Image Processing 1,Semester 1, Victoria University Of Technology: School of computer science and mathematics, 2004.\u003c/li\u003e\n \u003cli\u003eA. S.Sinha (2018), \u0026quot;Regression based integrated bi-sensor Sar Data Model to estimate forest carbon stock,\u0026quot; \u003cem\u003eJthenal Of Forestry Research ,\u0026nbsp;\u003c/em\u003e2018.\u003c/li\u003e\n \u003cli\u003eM. N. S.Sinha et al. (2015), \u0026quot;A review of radar remote sensing for biomass estimation,\u0026quot; \u003cem\u003eInternational Jthenal of Environmental science and technology ,\u0026nbsp;\u003c/em\u003e2015.\u003c/li\u003e\n \u003cli\u003eJulian D Colorado et al. (2020), \u0026quot;A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops,\u0026quot; \u003cem\u003ePLOS ONE ,\u0026nbsp;\u003c/em\u003e2020.\u003c/li\u003e\n \u003cli\u003eM. J. K. D. Monika Mierzwa‑Hersztek1 et al. (2019), \u0026quot;Assessment of energy parameters of biomass and biochars,\u0026quot; \u003cem\u003eJthenal of Material Cycles and Waste Management,\u0026nbsp;\u003c/em\u003epp. 786-800, 2019.\u003c/li\u003e\n \u003cli\u003eNaana Tetebea Oyirifi, Ruhiya Abubakar, Emmanuel Kwame Effah et al. \u0026ldquo;Embracing Environmentalism By Assessing The Sustainability and Adoption Of Biomass As Boiler Fuel In The Manufacturing Industry\u0026rdquo;, 09 July 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3972339/v1]\u003c/li\u003e\n \u003cli\u003eS. Zafar (2021), \u0026quot;Energy potential of coconut biomass,\u0026quot; \u003cem\u003eBioenergy consult,\u0026nbsp;\u003c/em\u003e2021.\u003c/li\u003e\n \u003cli\u003eV. K. Mishra, S. Kumar, and N. Shukla (2017), \u0026ldquo;Image Acquisition and Techniques to Perform Image Acquisition,\u0026rdquo;SAMRIDDHI A J. Phys. Sci. Eng. Technol., vol. 9, no. 01, pp. 21\u0026ndash;24, 2017\u003c/li\u003e\n \u003cli\u003eBhardwaj, N., Kaur, G., and Singh, P.K., \u0026ldquo;A systematic review on image enhancement techniques\u0026rdquo;,Sensors and Image Processing, Springer, Singapore, 227\u0026ndash;235, (2018)\u003c/li\u003e\n \u003cli\u003eGandhi, M., Kamdar, J. \u0026amp; Shah, M. Preprocessing of non-symmetrical images for edge detection. Augment. Hum. Res. 5, 1\u0026ndash;10(2020)\u003c/li\u003e\n \u003cli\u003eAbubakar, R., Kumar, K.S., Acakpovi, A., Ayinga, U.W., Prempeh, N.A., Tetteh, J. and Kumassah, E.S., 2020, December. Convolutional neural networks for solid waste segregation and prospects of waste-to-energy in ghana. In \u003cem\u003eProceedings of the 2nd African International Conference on Industrial Engineering and Operations Management Harare, Zimbabwe\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Greening, Waste Management, Pollution, Pyrolysis, Bioenergy","lastPublishedDoi":"10.21203/rs.3.rs-4812686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4812686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnergy in all forms is a key requirement for human livelihoods and socio-economic development. However, overreliance on a single sthece of energy can cause energy management issues because of the occurrence of system over-burdening. Thus, the utilization of other forms of energy is highly promoted worldwide, with a clear emphasis on enhancing environmentalism and reducing pollution through waste management and control. This paper considers the use of digital image processing in the form of pattern recognition to extract the pattern of a coconut shell and charcoal wood to show the correlation between their patterns and thus deduce the energy properties of the coconut shell.\u003c/p\u003e \u003cp\u003eA 3D camera is used to capture the digital image of the preprocessed coconut shell. The appropriate algorithm is then written on the MATLAB software toolbox to manipulate and translate the digital images, hence revealing its hidden nature. The technical process of scrutinizing the hidden properties involves the changes of pixels of the images, enhancement and thresholding; which is the pattern revealing step. Finally, the automatic pattern recognition toolbox acts to recognize the resemblance of the pattern of the coconut shells to the wood charcoal in order to analyze the pattern directly and determine the energy property percentage indices of the agent under test. The results indicate that coconut shells is carbon based, first- generation bioenergy crop and has high bioenergy properties and again a lower ignition property compared to charcoal wood.\u003c/p\u003e","manuscriptTitle":"Espousing Environmental Pollution Management and Control by Exploring the Bioenergy Properties of Coconut Shell Nanoparticles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 20:14:52","doi":"10.21203/rs.3.rs-4812686/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":"ca66e8e7-cd00-4059-bf00-aa69bcbc0f35","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35208080,"name":"Renewable Resources"}],"tags":[],"updatedAt":"2024-08-05T20:14:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 20:14:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4812686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4812686","identity":"rs-4812686","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}