Transforming Hill Agriculture through Artificial Intelligence and Machine Learning: A Case Study of Kumaun Region, Uttarakhand

Abstract

The Kumaun region of Uttarakhand is characterised by diverse agro-climatic conditions, fragmented landholdings, mountainous terrain, variable soil properties, rainfall dependence and heterogeneous cropping systems. These conditions pose serious challenges for traditional agricultural planning and resource management. The synergy of Artificial Intelligence (AI) and Machine Learning (ML) with remote sensing, Geographic Information Systems (GIS), the Internet of Things (IoT), and cloud computing offers promising opportunities to develop location-specific agricultural decision-support systems. In this paper, we review emerging AI/ML applications in Kumaun, such as crop-yield prediction, crop classification, agricultural land monitoring, soil-quality assessment, drought prediction, smart irrigation, and explainable AI. We conducted a systematic literature review and synthesis of relevant multidisciplinary research using PRISMA-2020. In Uttarakhand, the literature reports the use of Artificial Neural Networks, Random Forests, Support Vector Machines, Gradient Boosting, hybrid ML models and satellite-based approaches. However, the present work is fragmented and largely problem-specific. Thus, this study proposes an integrated Kumaun Agricultural AI Framework that combines satellite observations, IoT sensors, weather data, soil characteristics, crop phenology, ML/DL, Explainable AI (XAI), and Reinforcement Learning (RL). The framework will enable predictive and prescriptive decision-making, improve resource-use efficiency, boost climate resilience and promote sustainable agricultural development in the Kumaun region.