From Ground Truth to Digital Twins: AI-Driven Remote Sensing for Scalable Precision Agrochemical Management

Conventional uniform agrochemical application ignores the spatial and temporal varia-bility of soils, causing inefficient input use, yield loss, and environmental degradation. This review synthesizes advances in integrating Artificial Intelligence (AI) and Remote Sensing (RS) for precision soil and agrochemical management, providing a data-driven foundation for sustainable agriculture. By combining multispectral, hyperspectral, and radar data from Landsat-8/9, Sentinel-1/2, and UAV platforms, as examples, with ad-vanced AI algorithms—Random Forest, Support Vector Machines, Convolutional Neural Networks, and Physics-Informed Neural Networks—researchers can predict soil salinity, moisture, nutrients, and organic matter with accuracies often exceeding 90% as some studies indicates. These predictive maps delineate management zones that enable varia-ble-rate application of fertilizers and pesticides, enhancing efficiency and reducing leach-ing, runoff, and greenhouse gas emissions. The review highlights innovations in IoT-based soil sensors, Synthetic Aperture Radar, and multi-sensor data fusion, emphasizing the need for standardized data protocols, scalable AI frameworks, and supportive policy mechanisms. Integrating AI and RS trans-forms reactive agrochemical use into predictive, climate-smart management, improving soil health, resource efficiency, and food security while advancing global sustainability goals.