Geo-Intelligent Agriculture: Integrating GIS, Remote Sensing, and IoT for Real-Time Soil and Crop Health Monitoring and Predictive Farm Management

Abstract

Global agriculture is undergoing rapid technological transformation amid escalating environmental and systemic challenges, including soil degradation, climate variability, and declining resource efficiency. The convergence of Geographic Information Systems (GIS), Remote Sensing (RS), and the Internet of Things (IoT) has given rise to geo-intelligent agriculture, a data-driven framework that integrates spatial analytics, real-time sensing, and predictive modeling for sustainable farm management. This study synthesizes recent advances in spatially explicit decision-making, highlighting how multisource data fusion enables real-time monitoring of soil and crop health, early anomaly detection, and predictive diagnostics. GIS-based geostatistical modeling delineates site-specific management zones, while UAV and satellite-based remote sensing deliver continuous spectral and thermal insights into crop vigor and stress dynamics. Simultaneously, IoT-enabled sensor networks provide in-situ soil and microclimatic data streams that enhance adaptive irrigation, nutrient optimization, and pest management. The integration of these systems through cloud and edge computing creates a hyperconnected, self-learning agroecosystem capable of real-time diagnostics and localized decision support. Emerging frameworks such as Geo-Conscious Agriculture extend this paradigm by incorporating ethical data governance, explainable AI, and digital twin simulations to promote climate resilience and transparency. Overall, the fusion of geospatial intelligence, IoT, and AI represents a transformative pathway toward predictive, site-specific, and sustainable agriculture, redefining the relationship between technology, ecology, and food security.