Cotton yield prediction using drone derived LAI and chlorophyll content

The unmanned aerial vehicles (UAV) have become a better solution for agricultural growers due to advanced features such as minimal maintenance costs, quick set-up time, low acquisition and live data capturing. Near-ground remote sensing (drone) has opened up new agronomic opportunities crop management. This study predicted the seed cotton yield field area located at Tamil Nadu Agricultural University, Coimbatore. Pearson correlation analysis regression were done ground truth vegetation indices validation accuracy also find best-performing indices. It was concluded that Wide Dynamic Range Vegetation Index (WDRVI) showed coefficient (R=0.959) with LAI (R2=0.919). In contrast, Modified Chlorophyll Absorption Ratio (MCARI) (R=0.919) SPAD chlorophyll (R2=0.845). Then best performing WDRVI MCARI further used generating model. High spatial resolution drone imageries determining are reliable rapid, per study. helps determine scale their influence on production. prediction technical support widespread adoption application of vehicle in large-scale precision agriculture.