Geospatial Intelligence for Sustainable Agriculture: Integrating GIS and Image Processing

Abstract

The rapid growth of digitalization upon agriculture has established today Geographic Information Systems (GIS) and image processing as key technologies allowing to reinforce spatial intelligence, increase the precision of decision-making processes and optimize natural resource utilization. The present review is intended to provide an overview of the fundamentals, methodological developments, and recent applications related to GIS and image processing technologies focusing on their fusion for precision and smart farming. GIS provides an effective platform to manage, analyse and visualise geospatial data whilst image processing is used to quantitative extract crop, soil and environmental information from satellite, UAV and proximal sensing imagery. Altogether, these technologies are used for crop monitoring, vegetation index mapping, soil variability analysis, early disease and pest detection, stress diagnostics and data-driven site-specific management. The paper also discusses the increasing role of artificial intelligence and machine learning to automate feature extraction, classification and prediction modelling in complex geospatial datasets. However, challenges still exist in data interoperation, real-time analytics, algorithm scaling and integration of multi-source imagery. Filling these gaps is necessary to maximize the potential of GIS image processing approaches for developing climate-resilient, sustainable and high-efficiency agricultural systems. The study emphasizes that the merging geospatial technologies are provoking future of smart farming and enabling science-based agricultural revolutions worldwide.