Handheld Sensor-Based NDVI Measurement as an Alternative to Destructive Sampling for Growth and Yield Assessment in Maize

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Farooque, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5745556/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2025 Read the published version in International Journal of Plant Production → Version 1 posted 5 You are reading this latest preprint version Abstract The Normalized Difference Vegetation Index (NDVI) can be an indicator for plant growth response and ultimately yield to water and nitrogen (N) requirements in semi-arid environments. To assess the growth and development of maize through NDVI measurements under various N and irrigation water regimes, a field trial was conducted over two consecutive years at the University of Agriculture, Faisalabad, employing split-plot arrangement. The study involved three irrigation water regimes (IWR): normal irrigation, water deficit at the vegetative stage, and water deficit at the reproductive stage. Additionally, five N application rates (NR) were applied: 100, 150, 200, 250, and 300 kg ha − 1 during each study year. The canopy reflectance NDVI data was measured using a Handheld GreenSeeker at ten-day intervals. The results showed that deficit irrigation regimes reduced NDVI, with the maximum decrease observed in IWR3 during both growing seasons. The results also revealed that an increase in N fertilizer application rates led to higher values for NDVI, leaf area index, and total dry matter (TDM). A highly significant and positive correlation of LAI and TDM, was observed with NDVI under different irrigation regimes (ranging R 2 = 0.67 to 0.97) and N application rates (ranging R 2 = 0.58 to 0.97) throughout the entire growing season. Additionally, all other growth and yield parameters of maize showed a positive and significant correlation with the NDVI values. The study findings suggest that utilization of NDVI measured with a handheld GreenSeeker sensor can effectively determine the impact of irrigation and N on maize grown in a semi-arid environment. Therefore, handheld GreenSeeker sensor has emerged as a promising tool as a more efficient and rapid alternative method for non-destructively measuring maize growth and potentially assessing yield. Irrigation Water Regimes Maize Growth Assessment Nitrogen Application Rates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Maize ( Zea mays L), recognized as the third important crop worldwide, is considered one of the most drought-sensitive crops (Gaffney et al., 2015; Leng et al., 2021; Nguyen e al., 2023; Liu et al., 2024 ). The crop needs 500–800 mm of water during its life cycle of 80–110 days, depending upon the local weather conditions (Xie et al., 2017 ; Mazhar et al., 2021 ). As a C 4 crop, it utilizes water and nutrients more efficiently than most C 3 crops (Atumo and Ayalew, 2023 ; Wang, 2024 ). In semi-arid environments, the lack of rainfall ceases growth and development of maize crop due to the crop’s high water demand (Golzardi et al., 2017 ; Priyan et al., 2021). Thus, an adequate supply of irrigation water is important to optimize crop yield for semi-arid environments (Hammad et al., 2020 ; Jin et al., 2020 ; Wu et al., 2023 ). Water scarcity is one of the main limitations to crop production in semi-arid regions (Correia de Araujo et al., 2019; Golla, 2021 ; Karimi et al., 2024 ). Semi-arid regions are characterized as the regions receiving 250–500 mm annual precipitation (FAO, 2017 ; Choubin et al., 2017 ). The pace of water scarcity is increasing due to the high evaporation rate in semi-arid regions, heightening global apprehension about crop production. Water scarcity decreases the growth and development of crop plants by adversely affecting osmotic potential and rate of photosynthesis, ultimately resulting in a substantial decline in crop productivity (Farooq et al., 2009 ; Begna, 2020 ). Nitrogen is a major factor limiting yield and growth traits of maize in the semi-arid areas throughout the world (Priyan et al., 2021; Nguluu et al., 2023 ; Wu et al., 2024 ). It is a major component of amino acids, proteins, chlorophyll content, and hormones needed for optimal plant growth and development (Ohyama, 2010 ; Fathi, 2022 ). Nitrogen is also involved in dry matter accumulation (Cohen et al., 2019 ; Cui et al., 2019) and is vital for the formation of bold grains (Wu et al., 2022 ) due to its imperative role in the photosynthetic process. Previous studies have reported that N fertilizer application increases leaf chlorophyll contents, photosynthetic rate, and ultimately leaf area index and TDM accumulation of maize crop (Yu et al., 2021 ; Gao et al., 2023a , b ). Water scarcity has an impact on crop growth yield and N uptake and accumulation in crop (Coelho et al., 2020 ; Ran et al., 2023 ). Therefore, N deficiency is more severe in semi-arid areas where crop production exclusively depends on artificial irrigation water (Zhang et al., 2021; Li et al., 2010 ). Since river and rainfall water contain significant amounts of N, they serve as supplementary nutrients that enhance plant growth (Shahadha et al., 2021 ; Beusen et al., 2022 ). However, excessive application of N is causing contamination of ground water and soil acidification (Tolera et al., 2017 ; Manzoor et al., 2021 ). Therefore, efficient observing of plant water and N requirement, along with their appropriate management are essential for optimal crop production. There is also a critical need in many developing countries for reliable tools to measure the water and N demand of the in-season maize crop (Setiyono et al., 2011 ). Leaf N and chlorophyll contents are significant physiological characteristic for detecting crop health (Bassi et al., 2018 ; Shi et al., 2023 ; Gai et al., 2024 ). Recommendations for N fertilizer are usually determined by assessing soil N levels. Standard laboratory procedures for measuring soil N contents are time-consuming and costly; therefore, most farmers do not use them. Non-destructive methods to determine N rate with sensors use the N sufficiency index approach with a chlorophyll meter (Varvel et al., 2007 ; Ali and Salem, 2024 ; Hu et al., 2024 ). Similarly, several additional vegetation indices have been used to calculate N rate for maize using active canopy sensors data, such as the normalized difference vegetation index (NDVI) and the chlorophyll index (Solari et al., 2008 ; Kimaro et al., 2023 ). The application of NDVI has huge potential for timely crop evaluation and management without destructive plant sampling of the plants or using multiple soil sampling and analysis methods (Gerhards et al., 2019 ; Virnodkar et al., 2020 ; Weiss et al., 2020 ). In early research, NDVI was originally proposed by Tucker (1979) to study corn and soybean growth and development. It has been shown as a proven measure of plant growth under field conditions (Liu et al., 2017 ; Walsh et al., 2023 ; Stamford et al., 2023 ). The NDVI obtained from spectral reflectance relates to the photosynthetic activity of the overall crop (Gu et al., 2008 ; Basnyat et al., 2004 ). Studies have shown that the water and N status of field crops can be measured using leaf or canopy spectral reflectance data (Hammer et al., 2009; Croft et al., 2020 ; Wang et al., 2021 ). Recently, several investigations have explored the use of NDVI as a promising approach to measure plant growth for managing the nutrients demand of numerous crops (Dong et al., 2021 ; Burns et al., 2022 ). The handheld GreenSeeker is a sensor -based NDVI low-cost diagnostic tool developed in the early 1990s that can easily be used by extension workers and farmers to detect plant growth and plant nutrient demand (Lapidus et al., 2022 ; Maresma et al., 2020 ; Kimaro et al., 2023 ). It has been used in many countries, including Mexico, the United States, Pakistan, India, and China (Scheftic et al., 2014 ; Sultan et al., 2014; Liu et al., 2017 ). The active sensors are utilized to derive the NDVI of crops, thereby simulating the water and nitrogen demand of the crop based on the NDVI. To date, there has been limited research for measuring the effect of irrigation water and nitrogen on maize growth and development using the GreenSeeker in semi-arid environments. The hypothesis of this study was to develop a rapid and non-destructive method of evaluating maize growth and yield under different nutrient application using a handheld GreenSeeker. The specific objective of this study was to evaluate the growth and development of maize in semi-arid environments by measuring NDVI using a handheld GreenSeeker. Additionally, we analyzed the correlation between NDVI and the crop's growth and yield variables. Materials and Methods Experimental Design and Treatments A two-year field experiment was conducted in 2009 and in 2010 at the Research Farm, University of Agriculture Faisalabad, Pakistan (31° 26 ́ N, 73° 06 ́ E). The experimental design was a split-plot arrangement with three replications with water application regimes in the main plot and nitrogen rates in the subplots. The three irrigation water regimes were IWR 1 = normal irrigation [irrigation at V2, V6, V12, V16, VT, R1 and R3 stages (525 mm), IWR 2 = deficit water at vegetative stage [irrigation at V6, V12, VT, R1 and R3 stages (375 mm)], and IWR 3 = water deficit at reproductive stage [irrigation at V2, V6, V12, V16, VT and R3 stages (450 mm)]. The five N application rates were 100, 150, 200, 250, and 300 kg N ha −1 ). The N fertilizer was applied in three split doses (1/3 N at seedbed preparation, 1/3 N at V6, and 1/3 N at VT stages) according to each treatment. A buffer plot was kept among the main plots to avoid moisture effects from neighboring plots. Crop Management Practices The maize crop was planted at a seeding rate of 25 kg ha − 1 on August 1, 2009, and August 2, 2010. The net plot size was 300 cm by 500 cm, with a row spacing of 75 cm and a plant spacing of 20 cm within each row, and each plot consisted of 50 plants. The recommended doses of phosphorus (125 kg ha − 1 ) and potash (125 kg ha − 1 ), were incorporated in the soil during seedbed preparation. All experimental plots were managed with uniform standard cultural practices, except for the specific treatments being studied. Sampling Strategy A Handheld GreenSeeker Optical Sensor Unit (Model-505) was utilized to measure NDVI. Two central rows, each two meters long, in each experimental plot were monitored for NDVI and leaf area. Measurements were taken at ten day intervals between 9:00 AM and 11:00 AM. The sensor captured canopy reflectance in the red region (656 nm) and near-infrared (NIR) region (774 nm) to generate NDVI data. NDVI was calculated using Eq. (1), as described by Ali et al. ( 2014 ). \(\:\text{N}\text{D}\text{V}\text{I}=\frac{\left(\text{N}\text{I}\text{R}-\text{R}\text{E}\text{D}\right)}{\left(\text{N}\text{I}\text{R}+\text{R}\text{E}\text{D}\right)}\) Eq. (1) The sensor was over-headed the crop at a height of 0.3 m above the canopy during each measurement. The generated NDVI data from the sensor was then transmitted in sequence to a laptop computer for further analysis. The leaf area meter was used to measure leaf area of the representative leaf samples using the Eq. (2) prescribed by Watson (1952). \(\:\text{L}\text{A}\text{I}=\frac{\text{L}\text{e}\text{a}\text{f}\:\text{a}\text{r}\text{e}\text{a}}{\text{G}\text{r}\text{o}\text{u}\text{n}\text{d}\:\text{a}\text{r}\text{e}\text{a}}\) Eq. (2) Both NDVI and LAI data were collected nine times throughout the growing season in both years, from the seedling stage to physiological maturity (see Figs. 2 and 3 for details). Similarly, total dry matter (TDM) accumulation was measured at ten day intervals by randomly selecting and harvesting five plants from each plot. The harvested plants were oven-dried at 70 ˚C until a constant weight was achieved. Subsequently, the dry weight of the harvested plants was determined using an electric balance, and the TDM was recorded for both growing years. The LAI and TDM data in this study were used exclusively for correlation and regression analysis with NDVI (Figs. 5 and 6 ). The transpiration rate (TR) and photosynthetic rate (PR) of fully expanded leaves from randomized tagged plants in each plot were estimated during the vegetative and reproductive stages using a portable infrared gas-analyzer-based photosynthesis system (LCi Analyzer with Broad Head, part number LCi-002/B, serial number 32455). The portable Infra-Red Gas Analyzer (IRGA) operated under 3.5 mbar water vapor pressure in the leaf chamber, a leaf temperature ranges of 23.5°C to 35.5°C, 394.5 µmol mol⁻¹ ambient CO₂ concentration, and 58.5% relative humidity. These measurements were taken between 9:00 AM and 11:00 AM. The plant height (PH) of five randomly selected plants in each experimental plot was measured using a measuring tape. Similarly, five cobs from each plot were randomly selected, and their cob girth (CG), cob length (CL), and number of grains per cob (NGPC) were measured using standard protocols. Three samples of 1,000 grains, counted using a seed counter, were weighed on an analytical balance. The average of each variable was calculated for final representation across both growing years. Moreover, maize plants from an area of 1.5 meters in length from two central rows were harvested from each experimental unit and divided into leaves, stems, and cobs. The cobs were sun-dried and threshed using a small thresher, and the grain weight was recorded and converted to grain yield (kg ha − ¹). Statistical Analysis The effects of water and nitrogen application on the NDVI were analyzed by using analysis of variance in a statistical software SAS (SAS institute, 2004). The differences among the treatment means were considered significant at p ≤ 0.05. Sigma Plot was utilized to create graphs of the studied variables observed in time series data with ten days’ intervals and weather data. The standard deviation of all the variables was calculated using data from three replications. The statistical packages including readxl, cor and corplot of the R Studio were used for studying the association between the NDVI and all the studied variables of the maize during both growing years. Results and Discussion NDVI and Irrigation The results revealed that NDVI values were highly significantly (P < 0.05 to P < 0.007) affected by irrigation water regimes (IWR 1 , IWR 2 and IWR 3 ) during the entire growing season for both years (Fig. 2 a and b). NDVI values decreased when the crop was exposed to water deficit condition during the vegetative (IWR 2 ) and reproductive growth stages (IWR 3 ). Water deficit decreases the cell division and cell expansion, resulting in a reduction in crop growth (Farooq et al., 2008 ; Waraich and Ahmad, 2010 ; Zia et al., 2021 ) and crop canopy structure, that resulted in a decrease in NDVI values during water deficit conditions (IWR 2 and IWR 3 ). The increase in water supply during the vegetative growth stages of the maize crop accelerated plant growth and resulted in higher canopy structure (Cai et al., 2020 ). Thus, normal irrigation supply (IWR 1 ) resulted in higher NDVI values during both growing seasons. The maximum NDVI values were observed at 50 to 65 days after planting for all treatments during both growing seasons, and the values gradually decreased until maturity. Similarly, the same trend was observed in LAI that were reported in our previous findings for these experiments (Hammad et al., 2015 ). The same was also found by Scott et al. ( 2022 ), who found that NDVI increased linearly at early growing season and peaked at the vegetative growth stage using agronomic practices that were similar to our study. However, results revealed that there was no significant difference in NDVI values across all irrigation water regimes during the early stages of the crop. This may be due to rainfall in both years (Fig. 1 ). Additionally, water deficit conditions severely affected the maize crop during later crop growth stages compared to earlier growth stages. This might be attributed to its larger canopy size and increased water requirement during the later growth stages (Sah, et al., 2020 ). The gradual but significant decrease in NDVI, starting at 65 days after planting until maturity can be attributed to leaf senescence, a lower LAI, and canopy structure of the maize crop (Li et al., 2020 ). NDVI and Nitrogen Fertilizer Application Rates Nitrogen fertilizer application rates showed a significant (P < 0.05 to P < 0.002) effect on the NDVI values of maize (Fig. 3a and b) during both growing seasons. The NDVI values exhibited a linear increase with an increase in application of the N fertilizer. The highest NDVI values were observed with the application of 300 kg N ha − 1 , while the lowest values were recorded with the lower N application rate (100 kg N ha − 1 ). However, no significant differences in NDVI values were observed throughout the entire growing season by the application rates of 250 and 300 kg N ha − 1 . Previous studies have also reported that maize showed the highest growth and grain yield with application of 250 kg N ha − 1 for semi-arid environments (Alsharifi et al., 2022 ; Ahmad et al., 2022 ). Likewise, a similar trend was recorded in kernel growth of maize, as reported in our previous findings from the same experiment (Hammad et al., 2020 ). Therefore, the NDVI values can be correlated with grain yield. No significant difference was recorded in NDVI due to nitrogen application during the early and late growth stages. This could be attributed to the lower N requirement of plants during the early growth stages (Abendroth et al., 2011 ), and at later stages due to the onset of senescence and maturity (Zhao et al., 2024 ). Overall, the NDVI values showed an increasing trend for all treatments, reaching the highest value at 50 to 60 days after planting and then gradually decreasing as the crop senesced and approached maturity. The lowest NDVI values were recorded during the early season (20–30 DAP) for both years. These findings support the study of Jaberi-Aghdam et al. ( 2023 ), who reported that crop growth and NDVI increased with a gradual increase in N fertilizer rate as compared to lower N fertilizer application rates. Similarly, Zhao et al. ( 2023 ) reported that an increase in N fertilizer rate led to an increase in NDVI values during the early to mid-growth stages (peak vegetative growth stage), followed by a subsequent decrease. Thus, the NDVI can be used to estimate the crop growth and N fertilizer demand of the maize crop (Edalat et al., 2019). Correlation of NDVI with Maize Growth The correlation results showed that LAI was highly correlated with NDVI for different irrigation regime during the entire growing season with R 2 values ranging from 0.68 to 0.97 (Fig. 4 a and b). Similarly, the results revealed that LAI significantly and positively correlated with NDVI for different N application rate during both growing seasons with R 2 values ranging from 0.58 to 0.96 (Fig. 5 a and b). Previous research (Pfeffer et al., 2010 ; Zhao et al., 2023 ) also noted significant correlations between NDVI measurements and maize canopy growth at various N application rates. Additionally, studies have shown that NDVI measurement with a specific time interval may be capable of estimating green LAI with high accuracy, providing a suitable procedure for estimating LAI of crops with contrasting canopy architectures and leaf characteristics (Nguy-Robertson et al., 2012 ; Wang et al., 2021 ). The TDM significantly and positively correlated with NDVI under application of different irrigation regimes during both growing seasons with R 2 values ranging from 0.67 to 0.97 (Fig. 6 a and b). The results revealed that the TDM gradually increased during the growing season as all curves of TDM increase at y-axis (Fig. 7 a and b). Similarly, TDM was significantly and positively correlated with NDVI for different N application rates with R 2 values ranging from 0.78 to 0.98 (Fig. 7 a and b). However, TDM gradually increased during the growing season, while NDVI initially increased during the vegetative growth stages but then gradually decreased during reproductive stages around September 29 for both growing seasons. Hence from these findings it is suggested that the NDVI measurement is the best crop indicator for assessing the nitrogen application requirement in maize. Therefore, ground-based NDVI measurements using the handheld GreenSeeker are a best tool for precision agriculture and better crop growth and yield measurement (Satognon et al., 2021 ). The correlation results for grain yield showed a highly positive correlation with plants per square meter, photosynthetic and transpiration rate during both the vegetative and reproductive growth stages (Fig. 8a and b). Similarly, all growth and yield traits including plant height, cob girth, cob length, number of grains per cob, thousand grains weight, grain and biological yield, were positively and significantly correlated with NDVI during both growing seasons. The results are consistent with the findings of Tamás et al. (2023) who concluded that NDVI correlated with crop growth and yield components of maize. Conclusion The current study revealed that deficit irrigation water regimes resulted in a decrease in NDVI, with the most significant decline observed when the crop was subjected to water deficit during the reproductive phase. Among the nitrogen fertilizer application rates, the highest NDVI values were found for the 250 kg N ha − 1 rate. This suggests that NDVI is effective in detecting irrigation and nitrogen requirements for maize crops. Furthermore, regression analysis showed highly significant and positive correlations of growth and yield variables with NDVI, ranging R 2 values from 0.67 to 0.97. Thus, ground-based NDVI measurements using the handheld GreenSeeker prove to be an optimal tool for precision crop management. Declarations Competing Interests Statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was conducted as part of Hafiz M. Hammad's doctoral thesis at the University of Agriculture Faisalabad (UAF), Pakistan. The authors sincerely appreciate the Agro-Climatology Lab at UAF for providing the handheld GreenSeeker device used in the crop plant study. References Abendroth, L. J., Elmore, R. W., Boyer, M. J., & Marlay, S. K. (2011). Corn growth and development ( p. 49 ). Iowa State University of Science and Technology Cooperative Extension Service, Ames. 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Tables Table 1 The physical-chemical properties of the top soil layer (0-30 cm) prior to the start of the experiment in 2009 and 2010 Soil attributes 2009 2010 Soil pH 7.64 7.58 Organic matter (%) 1.02 1.03 Total soluble salt (%) 12.3 12.3 EC (dS m -1 ) 1.66 1.68 Nitrogen (g kg -1 ) 0.64 0.68 Phosphorous (g kg -1 ) 6.93 6.95 Potassium (g kg -1 ) 19.4 19.0 Sand (%) 60.0 59.0 Silt (%) 16.0 17.0 Clay (%) 24.0 24.0 Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2025 Read the published version in International Journal of Plant Production → Version 1 posted Editorial decision: Minor revisions 09 Mar, 2025 Reviewers agreed at journal 05 Feb, 2025 Reviewers invited by journal 14 Jan, 2025 Editor assigned by journal 07 Jan, 2025 First submitted to journal 05 Jan, 2025 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. 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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-5745556","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402052564,"identity":"5e1c81e5-d6db-42f7-980b-8003040b19ed","order_by":0,"name":"Hafiz Mohkum Hammad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACAwbGBweAdIIEA5AFZPDwEdbCbADTwmwA0sJGjBYGqBY2CRCLoBZz9sOMB3/m2ORJzshOq/yaYyfDxsD88NENPFose5IZDkhuSyuWlsjddlt2WzLQYWzGxjn4HHYg/8ABw22HE+eBtEhuYwZq4WGTxqvl/GOGA4lQLcWS2+qJ0HID6LCDQC2zgVoYP247TIyWxwwHG7elJc7sebtZmnHbcR42ZkJ+OZ/M/PHnNpvEGcdzNwIZ1fb87M0PH+PTggKYecAkscpBgPEHKapHwSgYBaNgxAAADsVMYoxQ4YsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1725-3069","institution":"Muhammad Nawaz Shareef University of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Hafiz","middleName":"Mohkum","lastName":"Hammad","suffix":""},{"id":402052565,"identity":"ea92b345-d0e5-49df-b45f-c156c1a31139","order_by":1,"name":"Ashfaq Ahmad","email":"","orcid":"","institution":"Ghazi University","correspondingAuthor":false,"prefix":"","firstName":"Ashfaq","middleName":"","lastName":"Ahmad","suffix":""},{"id":402052566,"identity":"03b2587b-e31c-4363-97ec-f0bcc764bc75","order_by":2,"name":"Farhat Abbas","email":"","orcid":"","institution":"University of Doha for Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Farhat","middleName":"","lastName":"Abbas","suffix":""},{"id":402052567,"identity":"4fc6a96d-662b-4210-92ac-28cb552d9947","order_by":3,"name":"Aitazaz A. Farooque","email":"","orcid":"","institution":"University of Prince Edward Island","correspondingAuthor":false,"prefix":"","firstName":"Aitazaz","middleName":"A.","lastName":"Farooque","suffix":""},{"id":402052568,"identity":"8103e5c2-1bde-40c0-98a4-d93da76d8f09","order_by":4,"name":"Carol Willkerson","email":"","orcid":"","institution":"Independent Scholar","correspondingAuthor":false,"prefix":"","firstName":"Carol","middleName":"","lastName":"Willkerson","suffix":""},{"id":402052569,"identity":"f5905d54-f6f1-4f16-8879-21896f53ada6","order_by":5,"name":"Saeed Ahmad","email":"","orcid":"","institution":"Muhammad Nawaz Sharif University of Agriculture: Muhammad Nawaz Shareef University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"","lastName":"Ahmad","suffix":""},{"id":402052570,"identity":"129fea97-028f-475b-82ae-81fd0f4abcd1","order_by":6,"name":"Gerrit Hoogenboom","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Gerrit","middleName":"","lastName":"Hoogenboom","suffix":""}],"badges":[],"createdAt":"2025-01-01 09:51:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5745556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5745556/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42106-025-00339-1","type":"published","date":"2025-04-14T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73954145,"identity":"60405373-078c-4f47-b097-cb3f1cc59202","added_by":"auto","created_at":"2025-01-16 10:13:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22637,"visible":true,"origin":"","legend":"\u003cp\u003eMeteorological data including daily total rainfall and maximum and minimum temperature recorded at the experimental site in 2009 (a) and 2010 (b)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/95ce7da60f3cb217320cc084.png"},{"id":73952955,"identity":"7e6e2b5d-9c25-4c7c-a672-4a741922a074","added_by":"auto","created_at":"2025-01-16 10:05:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16247,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of irrigation regimes on normalized difference vegetation index during the 2009 (a) and 2010 (b) growing seasons\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/d7d9ed09928e3a2a94a24231.png"},{"id":73954146,"identity":"582f07f5-0d35-43cd-9f86-a8a99db23302","added_by":"auto","created_at":"2025-01-16 10:13:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20576,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of N fertilizer application rates on normalized difference vegetation index during the 2009 (a) and 2010 (b) growing seasons\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/920736b3be51368e74f23fe0.png"},{"id":73954148,"identity":"7efffe67-3f8b-47a6-a675-eb82a3237272","added_by":"auto","created_at":"2025-01-16 10:13:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34351,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between normalized difference vegetation index (NDVI) and leaf area index (LAI) under different irrigation application regimes during the 2009 (a) and 2010 (b) growing seasons. The vertical error bar represents SD for NDVI, while the horizontal error bar represents SD for LAI\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/035114fb75e9504d6247cb0a.png"},{"id":73952961,"identity":"ca2eb372-8c3a-4a4c-b9b5-938c8f2215f7","added_by":"auto","created_at":"2025-01-16 10:05:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44299,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between normalized difference vegetation index and leaf area index (NDVI) under different N fertilizer application rates during the 2009 (a) and 2010 (b) growing seasons. The vertical error bar represents SD for NDVI, while the horizontal error bar represents SD for LAI\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/9bab254b68aaf20d369cac5e.png"},{"id":73954147,"identity":"5d2097cc-7b80-4136-aa2d-7924555725c2","added_by":"auto","created_at":"2025-01-16 10:13:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":39175,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between normalized difference vegetation index (NDVI) and TDM production (g m\u003csup\u003e-2\u003c/sup\u003e) under different irrigation application regimes during the 2009 (a) and 2010 (b) growing seasons. The vertical error bar represents SD for NDVI, while the horizontal error bar represents SD for TDM\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/34e61eba1efd9ac9a05e5e23.png"},{"id":73952956,"identity":"c00e9627-cb8a-43e6-b194-984ac7f66c02","added_by":"auto","created_at":"2025-01-16 10:05:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41791,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between normalized difference vegetation index and TDM production (g m\u003csup\u003e-2\u003c/sup\u003e) under different N fertilizer application rates during the 2009 (a) and 2010 (b) growing seasons. The vertical error bar represents SD for NDVI, while the horizontal error bar represents SD for TDM\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/29df4fbd87816679f18db973.png"},{"id":73954373,"identity":"d7e96480-a30a-43b9-8b27-aed42e0521bf","added_by":"auto","created_at":"2025-01-16 10:21:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":51352,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix analysis during the 2009 (a) and 2010 (b) growing seasons, among grain yield (GY), plants m\u003csup\u003e-2\u003c/sup\u003e (PMS), photosynthetic rate during the vegetative (PRV) and reproductive (PRR)\u0026nbsp; phase;\u0026nbsp; transpiration rate during the vegetative (TRV) and reproductive (TRR) phase, plant height (PH), cob girth (CG), cob length (CL), number of grains per cob (NGPC), thousand grain weight (TGW), biological yield (BY), number of days to maturity (NDM), and the Normalized difference vegetation index (NDVI)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/ebe122296275a567994586d9.png"},{"id":81050783,"identity":"dfdc0e3b-6041-4344-9e1d-192136185b64","added_by":"auto","created_at":"2025-04-21 16:04:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":864763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5745556/v1/5c9bbe34-73ba-477e-b64d-f83a6bcc9f8b.pdf"}],"financialInterests":"","formattedTitle":"Handheld Sensor-Based NDVI Measurement as an Alternative to Destructive Sampling for Growth and Yield Assessment in Maize","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L), recognized as the third important crop worldwide, is considered one of the most drought-sensitive crops (Gaffney et al., 2015; Leng et al., 2021; Nguyen e al., 2023; Liu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The crop needs 500\u0026ndash;800 mm of water during its life cycle of 80\u0026ndash;110 days, depending upon the local weather conditions (Xie et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mazhar et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a C\u003csub\u003e4\u003c/sub\u003e crop, it utilizes water and nutrients more efficiently than most C\u003csub\u003e3\u003c/sub\u003e crops (Atumo and Ayalew, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In semi-arid environments, the lack of rainfall ceases growth and development of maize crop due to the crop\u0026rsquo;s high water demand (Golzardi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Priyan et al., 2021). Thus, an adequate supply of irrigation water is important to optimize crop yield for semi-arid environments (Hammad et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Water scarcity is one of the main limitations to crop production in semi-arid regions (Correia de Araujo et al., 2019; Golla, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Karimi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Semi-arid regions are characterized as the regions receiving 250\u0026ndash;500 mm annual precipitation (FAO, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Choubin et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The pace of water scarcity is increasing due to the high evaporation rate in semi-arid regions, heightening global apprehension about crop production. Water scarcity decreases the growth and development of crop plants by adversely affecting osmotic potential and rate of photosynthesis, ultimately resulting in a substantial decline in crop productivity (Farooq et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Begna, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNitrogen is a major factor limiting yield and growth traits of maize in the semi-arid areas throughout the world (Priyan et al., 2021; Nguluu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is a major component of amino acids, proteins, chlorophyll content, and hormones needed for optimal plant growth and development (Ohyama, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fathi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nitrogen is also involved in dry matter accumulation (Cohen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cui et al., 2019) and is vital for the formation of bold grains (Wu et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) due to its imperative role in the photosynthetic process. Previous studies have reported that N fertilizer application increases leaf chlorophyll contents, photosynthetic rate, and ultimately leaf area index and TDM accumulation of maize crop (Yu et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Water scarcity has an impact on crop growth yield and N uptake and accumulation in crop (Coelho et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ran et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, N deficiency is more severe in semi-arid areas where crop production exclusively depends on artificial irrigation water (Zhang et al., 2021; Li et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Since river and rainfall water contain significant amounts of N, they serve as supplementary nutrients that enhance plant growth (Shahadha et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Beusen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, excessive application of N is causing contamination of ground water and soil acidification (Tolera et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Manzoor et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, efficient observing of plant water and N requirement, along with their appropriate management are essential for optimal crop production. There is also a critical need in many developing countries for reliable tools to measure the water and N demand of the in-season maize crop (Setiyono et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeaf N and chlorophyll contents are significant physiological characteristic for detecting crop health (Bassi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gai et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recommendations for N fertilizer are usually determined by assessing soil N levels. Standard laboratory procedures for measuring soil N contents are time-consuming and costly; therefore, most farmers do not use them. Non-destructive methods to determine N rate with sensors use the N sufficiency index approach with a chlorophyll meter (Varvel et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ali and Salem, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, several additional vegetation indices have been used to calculate N rate for maize using active canopy sensors data, such as the normalized difference vegetation index (NDVI) and the chlorophyll index (Solari et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kimaro et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The application of NDVI has huge potential for timely crop evaluation and management without destructive plant sampling of the plants or using multiple soil sampling and analysis methods (Gerhards et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Virnodkar et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Weiss et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In early research, NDVI was originally proposed by Tucker (1979) to study corn and soybean growth and development. It has been shown as a proven measure of plant growth under field conditions (Liu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Walsh et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Stamford et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The NDVI obtained from spectral reflectance relates to the photosynthetic activity of the overall crop (Gu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Basnyat et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Studies have shown that the water and N status of field crops can be measured using leaf or canopy spectral reflectance data (Hammer et al., 2009; Croft et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, several investigations have explored the use of NDVI as a promising approach to measure plant growth for managing the nutrients demand of numerous crops (Dong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Burns et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe handheld GreenSeeker is a sensor -based NDVI low-cost diagnostic tool developed in the early 1990s that can easily be used by extension workers and farmers to detect plant growth and plant nutrient demand (Lapidus et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maresma et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kimaro et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It has been used in many countries, including Mexico, the United States, Pakistan, India, and China (Scheftic et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sultan et al., 2014; Liu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The active sensors are utilized to derive the NDVI of crops, thereby simulating the water and nitrogen demand of the crop based on the NDVI. To date, there has been limited research for measuring the effect of irrigation water and nitrogen on maize growth and development using the GreenSeeker in semi-arid environments. The hypothesis of this study was to develop a rapid and non-destructive method of evaluating maize growth and yield under different nutrient application using a handheld GreenSeeker. The specific objective of this study was to evaluate the growth and development of maize in semi-arid environments by measuring NDVI using a handheld GreenSeeker. Additionally, we analyzed the correlation between NDVI and the crop's growth and yield variables.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Design and Treatments\u003c/h2\u003e \u003cp\u003eA two-year field experiment was conducted in 2009 and in 2010 at the Research Farm, University of Agriculture Faisalabad, Pakistan (31\u0026deg; 26 ́ N, 73\u0026deg; 06 ́ E). The experimental design was a split-plot arrangement with three replications with water application regimes in the main plot and nitrogen rates in the subplots. The three irrigation water regimes were IWR\u003csub\u003e1\u003c/sub\u003e = normal irrigation [irrigation at V2, V6, V12, V16, VT, R1 and R3 stages (525 mm), IWR\u003csub\u003e2\u003c/sub\u003e = deficit water at vegetative stage [irrigation at V6, V12, VT, R1 and R3 stages (375 mm)], and IWR\u003csub\u003e3\u003c/sub\u003e = water deficit at reproductive stage [irrigation at V2, V6, V12, V16, VT and R3 stages (450 mm)]. The five N application rates were 100, 150, 200, 250, and 300 kg N ha\u003csup\u003e\u0026minus;1\u003c/sup\u003e). The N fertilizer was applied in three split doses (1/3 N at seedbed preparation, 1/3 N at V6, and 1/3 N at VT stages) according to each treatment. A buffer plot was kept among the main plots to avoid moisture effects from neighboring plots.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCrop Management Practices\u003c/h3\u003e\n\u003cp\u003eThe maize crop was planted at a seeding rate of 25 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e on August 1, 2009, and August 2, 2010. The net plot size was 300 cm by 500 cm, with a row spacing of 75 cm and a plant spacing of 20 cm within each row, and each plot consisted of 50 plants. The recommended doses of phosphorus (125 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and potash (125 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), were incorporated in the soil during seedbed preparation. All experimental plots were managed with uniform standard cultural practices, except for the specific treatments being studied.\u003c/p\u003e\n\u003ch3\u003eSampling Strategy\u003c/h3\u003e\n\u003cp\u003eA Handheld GreenSeeker Optical Sensor Unit (Model-505) was utilized to measure NDVI. Two central rows, each two meters long, in each experimental plot were monitored for NDVI and leaf area. Measurements were taken at ten day intervals between 9:00 AM and 11:00 AM. The sensor captured canopy reflectance in the red region (656 nm) and near-infrared (NIR) region (774 nm) to generate NDVI data. NDVI was calculated using Eq.\u0026nbsp;(1), as described by Ali et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{N}\\text{D}\\text{V}\\text{I}=\\frac{\\left(\\text{N}\\text{I}\\text{R}-\\text{R}\\text{E}\\text{D}\\right)}{\\left(\\text{N}\\text{I}\\text{R}+\\text{R}\\text{E}\\text{D}\\right)}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;(1)\u003c/p\u003e \u003cp\u003eThe sensor was over-headed the crop at a height of 0.3 m above the canopy during each measurement. The generated NDVI data from the sensor was then transmitted in sequence to a laptop computer for further analysis.\u003c/p\u003e \u003cp\u003eThe leaf area meter was used to measure leaf area of the representative leaf samples using the Eq.\u0026nbsp;(2) prescribed by Watson (1952).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{A}\\text{I}=\\frac{\\text{L}\\text{e}\\text{a}\\text{f}\\:\\text{a}\\text{r}\\text{e}\\text{a}}{\\text{G}\\text{r}\\text{o}\\text{u}\\text{n}\\text{d}\\:\\text{a}\\text{r}\\text{e}\\text{a}}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;(2)\u003c/p\u003e \u003cp\u003eBoth NDVI and LAI data were collected nine times throughout the growing season in both years, from the seedling stage to physiological maturity (see Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and 3 for details). Similarly, total dry matter (TDM) accumulation was measured at ten day intervals by randomly selecting and harvesting five plants from each plot. The harvested plants were oven-dried at 70 ˚C until a constant weight was achieved. Subsequently, the dry weight of the harvested plants was determined using an electric balance, and the TDM was recorded for both growing years. The LAI and TDM data in this study were used exclusively for correlation and regression analysis with NDVI (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe transpiration rate (TR) and photosynthetic rate (PR) of fully expanded leaves from randomized tagged plants in each plot were estimated during the vegetative and reproductive stages using a portable infrared gas-analyzer-based photosynthesis system (LCi Analyzer with Broad Head, part number LCi-002/B, serial number 32455). The portable Infra-Red Gas Analyzer (IRGA) operated under 3.5 mbar water vapor pressure in the leaf chamber, a leaf temperature ranges of 23.5\u0026deg;C to 35.5\u0026deg;C, 394.5 \u0026micro;mol mol⁻\u0026sup1; ambient CO₂ concentration, and 58.5% relative humidity. These measurements were taken between 9:00 AM and 11:00 AM.\u003c/p\u003e \u003cp\u003eThe plant height (PH) of five randomly selected plants in each experimental plot was measured using a measuring tape. Similarly, five cobs from each plot were randomly selected, and their cob girth (CG), cob length (CL), and number of grains per cob (NGPC) were measured using standard protocols. Three samples of 1,000 grains, counted using a seed counter, were weighed on an analytical balance. The average of each variable was calculated for final representation across both growing years.\u003c/p\u003e \u003cp\u003eMoreover, maize plants from an area of 1.5 meters in length from two central rows were harvested from each experimental unit and divided into leaves, stems, and cobs. The cobs were sun-dried and threshed using a small thresher, and the grain weight was recorded and converted to grain yield (kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup1;).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe effects of water and nitrogen application on the NDVI were analyzed by using analysis of variance in a statistical software SAS (SAS institute, 2004). The differences among the treatment means were considered significant at p\u0026thinsp;\u0026le;\u0026thinsp;0.05. Sigma Plot was utilized to create graphs of the studied variables observed in time series data with ten days\u0026rsquo; intervals and weather data. The standard deviation of all the variables was calculated using data from three replications. The statistical packages including readxl, cor and corplot of the R Studio were used for studying the association between the NDVI and all the studied variables of the maize during both growing years.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNDVI and Irrigation\u003c/h2\u003e \u003cp\u003eThe results revealed that NDVI values were highly significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to P\u0026thinsp;\u0026lt;\u0026thinsp;0.007) affected by irrigation water regimes (IWR\u003csub\u003e1\u003c/sub\u003e, IWR\u003csub\u003e2\u003c/sub\u003e and IWR\u003csub\u003e3\u003c/sub\u003e) during the entire growing season for both years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and b). NDVI values decreased when the crop was exposed to water deficit condition during the vegetative (IWR\u003csub\u003e2\u003c/sub\u003e) and reproductive growth stages (IWR\u003csub\u003e3\u003c/sub\u003e). Water deficit decreases the cell division and cell expansion, resulting in a reduction in crop growth (Farooq et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Waraich and Ahmad, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zia et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and crop canopy structure, that resulted in a decrease in NDVI values during water deficit conditions (IWR\u003csub\u003e2\u003c/sub\u003e and IWR\u003csub\u003e3\u003c/sub\u003e). The increase in water supply during the vegetative growth stages of the maize crop accelerated plant growth and resulted in higher canopy structure (Cai et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, normal irrigation supply (IWR\u003csub\u003e1\u003c/sub\u003e) resulted in higher NDVI values during both growing seasons. The maximum NDVI values were observed at 50 to 65 days after planting for all treatments during both growing seasons, and the values gradually decreased until maturity. Similarly, the same trend was observed in LAI that were reported in our previous findings for these experiments (Hammad et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The same was also found by Scott et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found that NDVI increased linearly at early growing season and peaked at the vegetative growth stage using agronomic practices that were similar to our study. However, results revealed that there was no significant difference in NDVI values across all irrigation water regimes during the early stages of the crop. This may be due to rainfall in both years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, water deficit conditions severely affected the maize crop during later crop growth stages compared to earlier growth stages. This might be attributed to its larger canopy size and increased water requirement during the later growth stages (Sah, et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The gradual but significant decrease in NDVI, starting at 65 days after planting until maturity can be attributed to leaf senescence, a lower LAI, and canopy structure of the maize crop (Li et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNDVI and Nitrogen Fertilizer Application Rates\u003c/h3\u003e\n\u003cp\u003eNitrogen fertilizer application rates showed a significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to P\u0026thinsp;\u0026lt;\u0026thinsp;0.002) effect on the NDVI values of maize (Fig.\u0026nbsp;3a and b) during both growing seasons. The NDVI values exhibited a linear increase with an increase in application of the N fertilizer. The highest NDVI values were observed with the application of 300 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while the lowest values were recorded with the lower N application rate (100 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). However, no significant differences in NDVI values were observed throughout the entire growing season by the application rates of 250 and 300 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Previous studies have also reported that maize showed the highest growth and grain yield with application of 250 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for semi-arid environments (Alsharifi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Likewise, a similar trend was recorded in kernel growth of maize, as reported in our previous findings from the same experiment (Hammad et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the NDVI values can be correlated with grain yield. No significant difference was recorded in NDVI due to nitrogen application during the early and late growth stages. This could be attributed to the lower N requirement of plants during the early growth stages (Abendroth et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and at later stages due to the onset of senescence and maturity (Zhao et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the NDVI values showed an increasing trend for all treatments, reaching the highest value at 50 to 60 days after planting and then gradually decreasing as the crop senesced and approached maturity. The lowest NDVI values were recorded during the early season (20\u0026ndash;30 DAP) for both years. These findings support the study of Jaberi-Aghdam et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who reported that crop growth and NDVI increased with a gradual increase in N fertilizer rate as compared to lower N fertilizer application rates. Similarly, Zhao et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that an increase in N fertilizer rate led to an increase in NDVI values during the early to mid-growth stages (peak vegetative growth stage), followed by a subsequent decrease. Thus, the NDVI can be used to estimate the crop growth and N fertilizer demand of the maize crop (Edalat et al., 2019).\u003c/p\u003e\n\u003ch3\u003eCorrelation of NDVI with Maize Growth\u003c/h3\u003e\n\u003cp\u003eThe correlation results showed that LAI was highly correlated with NDVI for different irrigation regime during the entire growing season with R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.68 to 0.97 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and b). Similarly, the results revealed that LAI significantly and positively correlated with NDVI for different N application rate during both growing seasons with R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.58 to 0.96 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and b). Previous research (Pfeffer et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also noted significant correlations between NDVI measurements and maize canopy growth at various N application rates. Additionally, studies have shown that NDVI measurement with a specific time interval may be capable of estimating green LAI with high accuracy, providing a suitable procedure for estimating LAI of crops with contrasting canopy architectures and leaf characteristics (Nguy-Robertson et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe TDM significantly and positively correlated with NDVI under application of different irrigation regimes during both growing seasons with R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.67 to 0.97 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and b). The results revealed that the TDM gradually increased during the growing season as all curves of TDM increase at y-axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and b). Similarly, TDM was significantly and positively correlated with NDVI for different N application rates with R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.78 to 0.98 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and b). However, TDM gradually increased during the growing season, while NDVI initially increased during the vegetative growth stages but then gradually decreased during reproductive stages around September 29 for both growing seasons. Hence from these findings it is suggested that the NDVI measurement is the best crop indicator for assessing the nitrogen application requirement in maize. Therefore, ground-based NDVI measurements using the handheld GreenSeeker are a best tool for precision agriculture and better crop growth and yield measurement (Satognon et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation results for grain yield showed a highly positive correlation with plants per square meter, photosynthetic and transpiration rate during both the vegetative and reproductive growth stages (Fig.\u0026nbsp;8a and b). Similarly, all growth and yield traits including plant height, cob girth, cob length, number of grains per cob, thousand grains weight, grain and biological yield, were positively and significantly correlated with NDVI during both growing seasons. The results are consistent with the findings of Tam\u0026aacute;s et al. (2023) who concluded that NDVI correlated with crop growth and yield components of maize.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study revealed that deficit irrigation water regimes resulted in a decrease in NDVI, with the most significant decline observed when the crop was subjected to water deficit during the reproductive phase. Among the nitrogen fertilizer application rates, the highest NDVI values were found for the 250 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e rate. This suggests that NDVI is effective in detecting irrigation and nitrogen requirements for maize crops. Furthermore, regression analysis showed highly significant and positive correlations of growth and yield variables with NDVI, ranging R\u003csup\u003e2\u003c/sup\u003e values from 0.67 to 0.97. Thus, ground-based NDVI measurements using the handheld GreenSeeker prove to be an optimal tool for precision crop management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement:\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of Hafiz M. Hammad\u0026apos;s doctoral thesis at the University of Agriculture Faisalabad (UAF), Pakistan. The authors sincerely appreciate the Agro-Climatology Lab at UAF for providing the handheld GreenSeeker device used in the crop plant study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbendroth, L. J., Elmore, R. W., Boyer, M. J., \u0026amp; Marlay, S. K. 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Ethephon reduces maize nitrogen uptake but improves nitrogen utilization in \u003cem\u003eZea mays\u003c/em\u003e L. \u003cem\u003eFrontiers in Plant Science, 12\u003c/em\u003e, 762736. doi.10.3389/fpls.2021.762736.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, J., Qi, Y., Yin, C., \u0026amp; Liu, X. (2024). Effects of nitrogen reduction at different growth stages on maize water and nitrogen utilization under shallow buried drip fustigated Irrigation. \u003cem\u003eAgronomy, 14\u003c/em\u003e(1), 63. doi.10.3390/agronomy14010063.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, X., Wang, S., Wen, T., Xu, J., Huang, B., Yan, S., Gao, G., Zhao, Y., Li, H., Qiao, J., \u0026amp; Yang, J. (2023). On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies. \u003cem\u003eOpen Life Science, 18\u003c/em\u003e, 20220566. doi.10.1515/biol-2022-0566.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZia, R., Nawaz, M. S., Siddique, M. J., Hakim, S., \u0026amp; Imran, A. (2021). Plant survival under drought stress: Implications, adaptive responses, and integrated rhizosphere management strategy for stress mitigation. \u003cem\u003eMicrobiological Research, 242\u003c/em\u003e, 126626. doi.10.1016/j.micres.2020.126626.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eThe physical-chemical properties of the top soil layer (0-30 cm) prior to the start of the experiment in 2009 and 2010\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil attributes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eSoil pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eOrganic matter (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eTotal soluble salt (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eEC (dS m\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eNitrogen (g kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003ePhosphorous (g kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003ePotassium (g kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eSand (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eSilt\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5949%;\"\u003e\n \u003cp\u003eClay\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.7025%;\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-plant-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijpo","sideBox":"Learn more about [International Journal of Plant Production](https://link.springer.com/journal/42106)","snPcode":"42106","submissionUrl":"https://www.editorialmanager.com/ijpo/default2.aspx","title":"International Journal of Plant Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Irrigation Water Regimes, Maize Growth Assessment, Nitrogen Application Rates","lastPublishedDoi":"10.21203/rs.3.rs-5745556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5745556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) can be an indicator for plant growth response and ultimately yield to water and nitrogen (N) requirements in semi-arid environments. To assess the growth and development of maize through NDVI measurements under various N and irrigation water regimes, a field trial was conducted over two consecutive years at the University of Agriculture, Faisalabad, employing split-plot arrangement. The study involved three irrigation water regimes (IWR): normal irrigation, water deficit at the vegetative stage, and water deficit at the reproductive stage. Additionally, five N application rates (NR) were applied: 100, 150, 200, 250, and 300 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during each study year. The canopy reflectance NDVI data was measured using a Handheld GreenSeeker at ten-day intervals. The results showed that deficit irrigation regimes reduced NDVI, with the maximum decrease observed in IWR3 during both growing seasons. The results also revealed that an increase in N fertilizer application rates led to higher values for NDVI, leaf area index, and total dry matter (TDM). A highly significant and positive correlation of LAI and TDM, was observed with NDVI under different irrigation regimes (ranging R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.67 to 0.97) and N application rates (ranging R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.58 to 0.97) throughout the entire growing season. Additionally, all other growth and yield parameters of maize showed a positive and significant correlation with the NDVI values. The study findings suggest that utilization of NDVI measured with a handheld GreenSeeker sensor can effectively determine the impact of irrigation and N on maize grown in a semi-arid environment. Therefore, handheld GreenSeeker sensor has emerged as a promising tool as a more efficient and rapid alternative method for non-destructively measuring maize growth and potentially assessing yield.\u003c/p\u003e","manuscriptTitle":"Handheld Sensor-Based NDVI Measurement as an Alternative to Destructive Sampling for Growth and Yield Assessment in Maize","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-16 10:05:08","doi":"10.21203/rs.3.rs-5745556/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2025-03-09T22:21:32+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-02-05T05:51:02+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-14T14:13:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-07T07:38:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Plant Production","date":"2025-01-06T02:17:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-plant-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijpo","sideBox":"Learn more about [International Journal of Plant Production](https://link.springer.com/journal/42106)","snPcode":"42106","submissionUrl":"https://www.editorialmanager.com/ijpo/default2.aspx","title":"International Journal of Plant Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a4d599ca-fb13-429b-93c2-3b8311b81e8f","owner":[],"postedDate":"January 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T15:59:36+00:00","versionOfRecord":{"articleIdentity":"rs-5745556","link":"https://doi.org/10.1007/s42106-025-00339-1","journal":{"identity":"international-journal-of-plant-production","isVorOnly":false,"title":"International Journal of Plant Production"},"publishedOn":"2025-04-14 15:57:15","publishedOnDateReadable":"April 14th, 2025"},"versionCreatedAt":"2025-01-16 10:05:08","video":"","vorDoi":"10.1007/s42106-025-00339-1","vorDoiUrl":"https://doi.org/10.1007/s42106-025-00339-1","workflowStages":[]},"version":"v1","identity":"rs-5745556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5745556","identity":"rs-5745556","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}