Unlocking AI’s Potential in Agriculture: The Critical Role of Data

Abstract

India generates substantial volumes of public agricultural data, yet artificial intelligence (AI) adoption in farming remains limited and largely confined to pilot initiatives. This paper examines this gap by assessing India’s agricultural data infrastructure against the requirements of AI systems deployed at scale. Drawing on a systematic review of major national datasets and digital initiatives—including Soil Health Cards, crop insurance, AgriStack, and selected state platforms—we identify persistent structural constraints, including temporal misalignment between data collection and agricultural decision cycles, spatial fragmentation arising from the absence of common geocodes linking soil, weather, and yield information, limited machine readability due to reliance on static data formats, and unclear governance frameworks that restrict data access and reuse. These deficiencies impede cross-dataset integration and automated decision support, with disproportionate consequences for smallholders, who constitute 86% of India’s farmers and lack the capacity to compensate for weak data infrastructure. Drawing on implementation evidence from India and comparative international experiences, the paper identifies recurring features associated with scalable digital agriculture systems, including incentives linked to data provision, service bundling through local institutions, and sensor-enabled risk management. Building on these insights, we define the core attributes of AI-ready agricultural data—persistent identifiers, machine-accessible formats, temporal alignment, semantic interoperability, and transparent governance—and show that even India’s most advanced initiatives remain only partially aligned with these requirements. The paper’s contribution is primarily diagnostic: it demonstrates that data infrastructure limitations, rather than algorithmic capability, constitute the principal bottleneck to scalable AI adoption in Indian agriculture and proposes an operational framework for assessing agricultural data readiness.