Advancements in GIS for precision agriculture: Enhancing soil management and crop yield prediction

Abstract

Precision agriculture has emerged as a transformative paradigm in modern farming, driven by the integration of Geographic Information Systems (GIS), remote sensing, and data analytics. This review paper critically examines the role of GIS in optimising soil management and enhancing the accuracy of crop yield predictions. We analyse how spatial data infrastructures enable the transition from uniform field management to site-specific interventions, thereby improving resource efficiency and sustainability. The discussion encompasses the application of geostatistics to map soil nutrient variability, the deployment of Variable Rate Technology (VRT) for precise input application, and the use of multi-temporal satellite imagery to monitor crop phenology. Furthermore, we evaluate integrating machine learning algorithms with geospatial datasets to refine yield forecasting models. Special emphasis is placed on the Indian agricultural context, highlighting how GIS interventions address challenges related to small landholdings and climatic variability. The review concludes that while technical and economic barriers persist, the convergence of GIS with emerging technologies such as the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) offers a robust pathway toward global food security and climate-resilient agriculture.