Machine Learning Models for Agro-Climatic Decision Support Systems in Indian Agriculture

Abstract

Indian agriculture, a cornerstone of the national economy and the primary source of livelihood for a majority of its population, faces unprecedented challenges due to increasing climate variability and the structural constraints of smallholder farming. Agro-Climatic Decision Support Systems (DSS) offer a promising pathway to enhance resilience and productivity by providing timely, data-driven insights. This paper investigates the application of diverse Machine Learning (ML) models as the core intelligence engine for such DSS tailored to the Indian context. The objective is to comprehensively review current ML applications in Indian agriculture, propose a conceptual ML-DSS pipeline leveraging heterogeneous national data sources (including meteorological, soil health, remote sensing, and agricultural statistics), critically analyze the pertinent challenges impeding widespread adoption, and identify key future research directions. The analysis reveals that while ML techniques, ranging from traditional algorithms like Random Forest and Support Vector Machines to advanced deep learning architectures like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers, demonstrate significant potential for optimizing critical farming decisions—such as crop selection, yield forecasting, pest and disease management, and resource optimization—substantial hurdles remain. These challenges primarily revolve around India's complex data ecosystem, characterized by fragmentation, lack of standardization, variable quality, and difficulties in multimodal data integration. Furthermore, issues of model localization for diverse agro-climatic zones, scalability, and ensuring digital inclusion for smallholder farmers present significant barriers. Overcoming these requires a multi-pronged approach involving technological innovation (e.g., Federated Learning, Edge ML, Natural Language Processing), robust data governance frameworks, and targeted capacity building. Ultimately, well-designed ML-driven DSS are vital tools for navigating climate uncertainty, bolstering food security, enhancing the sustainability of agricultural practices, and improving the economic well-being of India's farmers.