Pantnagar Journal of Research

Abstract

The recent advances in the Artificial Intelligence have accelerated the growth process and agriculture is no exception. A number of AI based applications are knocking the door and ready to take the world by storm. However, AI/ML applications in Agriculture will hardly be of any pragmatic use unless it is supported by spatial database. Geospatial technology is the best tool to capture large scale data set at varied spatiotemporal resolutions and lays the foundation for transforming modern agriculture by enabling precision farming, real time monitoring and data driven decision making for sustainable resource management. This review presents an overview of recent advances in geospatial technologies and their applications in agriculture, with particular emphasis on the Indian context. The study discusses the role of remote sensing, Geographic Information Systems (GIS), unmanned aerial vehicles (UAVs), artificial intelligence (AI), machine learning (ML), cloud computing and geospatial analytics in agricultural monitoring and management. The integration of multi-source Earth Observation data from satellite missions such as Landsat, Sentinel, MODIS, Resources at, RISAT and emerging hyperspectral and thermal missions has significantly improved crop monitoring, yield prediction, digital soil mapping, disease and pest detection, soil moisture assessment and agricultural water management. AI and ML based approaches have enhanced the accuracy and efficiency of geospatial analyses, enabling predictive and automated agricultural applications. The review also highlights major challenges, including data quality, cloud contamination, limited ground truth observations, model interpretability and scalability for smallholder farming systems. Furthermore, ongoing government initiatives such as AgriStack, PMFBY, FASAL, CHAMAN and KrishiDSS are discussed in the context of digital agriculture development in India. Future trends indicate a transition toward UAV based real time farm monitoring, digital twins and climate resilient predictive farming systems, supporting sustainable agricultural production, improved resource use efficiency and enhanced food security.