AI-BASED SPATIO-TEMPORAL ANALYSIS FOR PREDICTING CLIMATE-RESILIENT CROP YIELDS IN INDIAN AGRICULTURAL SYSTEMS

Abstract

The accelerating impacts of climate change on Indian agriculture demand adaptive, data-driven methods for sustainable crop management. This study develops an AI-based spatio-temporal predictive framework to estimate climate-resilient crop yields by integrating multi-source datasets meteorological parameters, soil moisture, satellite-derived vegetation indices (NDVI, EVI, LST), and socio-agronomic inputs. Using machine learning and deep learning models such as Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks, the system analyzes historical data across key agro-climatic zones of India to forecast yield fluctuations under varying climatic conditions. The results indicate that LSTM models outperform traditional regression-based methods, achieving an accuracy improvement of over 18% in yield prediction and effectively capturing non-linear temporal dependencies. Spatial pattern analysis reveals high climate vulnerability in rain-fed regions of Maharashtra and central India, while irrigated northern plains exhibit relative yield stability. The integration of AI and remote sensing provides a scalable and near-real-time decision support tool for policy formulation, crop insurance planning, and climate adaptation strategies. This approach underscores the transformative potential of artificial intelligence in fostering resilient agricultural systems and achieving food security amid climatic uncertainties.