Integrating Geography and AI in Precision Agriculture: A Study of Soil Health and Crop Stress

Abstract Precision farming, an advanced agricultural practice, leverages technologies such as Artificial Intelligence (AI), Remote Sensing, and the Internet of Things (IoT) to maximize yield and resource efficiency. Among its many applications, AI plays a crucial role in monitoring soil health and detecting crop stress. This paper explores the integration of AI in precision farming, focusing on how machine learning, deep learning, and image processing techniques are used to assess soil conditions and identify crop stress factors such as nutrient deficiency, pest attacks, and water scarcity. The study further investigates the benefits, limitations, case studies, and policy recommendations for AI adoption, particularly in the context of Indian agriculture. This paper investigates the transformative role of Artificial Intelligence (AI) in advancing precision agriculture, with a specific focus on soil health monitoring and crop stress detection. By integrating AI technologies such as machine learning, deep learning, image processing, and geospatial analytics, the study demonstrates how modern agriculture can overcome traditional limitations related to soil degradation, resource inefficiency, and environmental challenges. Drawing upon case studies from Indian regions like Vidarbha and the Cauvery delta, the paper examines practical applications of AI tools—ranging from drone imagery and satellite data to mobile applications—for early disease detection, nutrient analysis, and irrigation management. It also discusses the socio-economic benefits, barriers to adoption, and policy recommendations for inclusive AI implementation in rural farming communities. The findings underscore AI's potential to drive sustainable, scalable, and data-driven agricultural practices essential for food security in the era of climate change.