Artificial Intelligence and Smart Sensing for Real-Time Soil Health Monitoring: Advancing Sustainable Watershed Management in India

Abstract

Healthy soil largely determines how a watershed holds, cleans and releases water, yet most Indian catchments are still judged from a handful of laboratory samples that arrive late and describe little of the landscape. This review, reported according to PRISMA 2020, draws on 29 studies identified through a structured search, supported by wider background literature, to examine how artificial intelligence (AI) and smart sensing can enable real-time soil health monitoring for sustainable watershed management in India. We consider the soil indicators that matter at catchment scale, the in situ, proximal and remote platforms that measure them, ways of fusing their outputs, machine learning and deep learning methods, and decision support tools. No single sensor proves adequate. In situ networks supply ground truth, proximal spectroscopy and electromagnetic induction add spatial detail, and satellite and airborne data provide regional context. AI can reconcile these layers, but only when data are co-registered, models are calibrated locally and uncertainty is reported openly. In India, fragmented landholdings, monsoon variability, sensor drift, poor connectivity, recurring costs and weak institutional capacity are the principal obstacles. A hybrid design, combining few permanent nodes with periodic mobile sampling, funded maintenance and shared data standards, appears most workable. Research priorities follow.