Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize

Pradosh Kumar Parida (), Eagan Somasundaram (), Ramanujam Krishnan, Sengodan Radhamani, Uthandi Sivakumar, Ettiyagounder Parameswari, Rajagounder Raja, Silambiah Ramasamy Shri Rangasami, Sundapalayam Palanisamy Sangeetha and Ramalingam Gangai Selvi
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Pradosh Kumar Parida: Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Eagan Somasundaram: Directorate of Agribusiness Development (DABD), Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Ramanujam Krishnan: Nammazhvar Organic Farming Research Centre, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Sengodan Radhamani: Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Uthandi Sivakumar: Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Ettiyagounder Parameswari: Nammazhvar Organic Farming Research Centre, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Rajagounder Raja: ICAR-Central Institute for Cotton Research (CICR) Regional Station, Coimbatore 641003, Tamil Nadu, India
Silambiah Ramasamy Shri Rangasami: Department of Forage Crop, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Sundapalayam Palanisamy Sangeetha: Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Ramalingam Gangai Selvi: Department of Physical Sciences & Information Technology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India

Agriculture, 2024, vol. 14, issue 7, 1-20

Abstract: Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R 2 ), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R 2 and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R 2 values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R 2 values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.

Keywords: remote sensing; multispectral images; leaf area index; chlorophyll value; stepwise regression; maize (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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