Rice yield prediction through drone-derived vegetation indices: A case study in Tamil Nadu, India

Abstract

Precision farming has been revolutionized by advancements in drone technology and remote sensing, enabling high accuracy in real-time crop monitoring and yield prediction. To explore the potential of drone-based remote sensing for predicting the rice yield by the assessment of vegetation indice (VI) were generated and analyzed to identify the most sensitive indices for predicting Laef Area Index (LAI), chlorophyll content and biomass. The experiment was conducted in two seasons, Kuruvai (July - November 2023) and Navarai (December 2023 - March 2024). In Kuruvai 2023, a positive correlation was observed between vegetation indices, Wide Dynamic Range Vegetation Index (WDRVI), Modified Chlorophyll and Reflectance Index (MCARI) and Modified Soil Adjusted Vegetation Index (MSAVI) with ground truth biophysical parameters, while Navarai season Perpendicular Vegetation Index (PVI), Modified Chlorophyll and Reflectance Index (MCARI) and Green Normalized Difference Vegetation Index (GNDVI) exhibited the highest positive correlation. The multiple linear regression analysis revealed that a combined model incorporating LAI, SPAD chlorophyll and biomass registered the highest R2 values of 0.792 and 0.800 for the Kuruvai and Navarai seasons. The predicted yield was positively correlated with the real-time yield with R2 values of 0.819 and 0.803 for both seasons. This study underscores the potential of drone-based VIs for precise yield prediction, offering a scalable and non-destructive method to enhance agricultural productivity and support decision-making in precision farming. Future research should focus on refining these models for broader applications across crops and agro-climatic conditions.