Understanding crop phenology and health through the use of remote sensing tools has gained increasing attention in precision agriculture. This study focuses on a 0.5-acre agricultural plot located at Tamil Nadu Agricultural University (TNAU), Coimbatore, where cotton was cultivated from March 11, 2022, to September 23, 2022. Sentinel-2 imagery was processed using Google Earth Engine (GEE) to derive NDVI (Normalized Difference Vegetation Index) values throughout the crop growth period. A time series analysis of mean NDVI values was conducted to observe the phenological stages of the cotton crop. Post-harvest, maize was cultivated in the same field. An NDVI image captured during this period revealed spatial variability in crop health across the plot. Ground truth photographs confirmed that certain areas exhibited poor crop vigor, aligning with low NDVI values. This study demonstrates the practical application of open-source satellite data and cloud-based platforms, such as GEE, for micro-level crop monitoring and health assessment in precision farming practices.
Raja et al. (Wed,) studied this question.