From Soil to Server: How AI-Driven Decision Systems are Reshaping Smallholder Farming

Abstract

Artificial intelligence (AI) is increasingly transforming agricultural systems, particularly in developing economies where smallholder farmers dominate food production. In India, smallholders face persistent challenges such as climate variability, limited access to information, and inefficient market linkages. This study examines how AI-driven decision support systems are reshaping smallholder farming practices, with a focus on qualitative insights derived from Indian case studies. Using an exploratory qualitative research design, the study draws on secondary data sources, including policy reports, academic literature, and documented case studies of AI-enabled agricultural platforms such as DeHaat, CropIn, and AI-based advisory systems implemented in Telangana and Tamil Nadu. The findings indicate that AI technologies significantly enhance decision-making by providing real-time, data-driven insights related to crop selection, irrigation, pest management, and market pricing. These systems contribute to increased productivity, reduced input costs, and improved resource efficiency. However, the study also identifies critical challenges, including digital illiteracy, infrastructural gaps, data reliability issues, and uneven access to technology among marginal farmers. The paper argues that AI in agriculture should be viewed as a socio-technical transformation rather than merely a technological innovation. For sustainable and inclusive impact, policy interventions must focus on accessibility, localization, and capacity building. The study contributes to the growing literature by providing context-specific qualitative insights into AI adoption in smallholder farming systems.