A Big Data Curation Framework for Precision Agriculture: Actions, Actors and Challenges in the Indian Context

Abstract

Precision agriculture produces large volumes of heterogeneous data from IoT sensor networks, satellite imagery, unmanned aerial surveys, crop registries and weather stations. India has committed substantial public investment to agricultural data infrastructure under the Digital Agriculture Mission, which by August 2026 had generated more than 10.31 crore Farmer IDs and surveyed over 31 crore plots. No published framework specifies what curation these data require, which institutions are responsible for each activity, or what obstacles arise in the Indian operating environment. This study develops such a framework through a systematic review of 214 sources published between January 2015 and August 2026, drawn from five bibliographic databases and supplemented by Government of India policy documents. Two coders agreed on 88.5 per cent of coding decisions for a random subsample of 50 records. The framework specifies curation actions at eight stages of the agricultural data lifecycle, assigns each action to one or more of nine institutional actor categories, and documents the associated challenges. Three findings distinguish precision agriculture from the general big data case: raster imagery requires curation procedures with no counterpart in text or sensor-stream data; machine-learning models function as curation objects rather than analytical tools alone; and metadata operating only in English cannot support a national agricultural data system in a country with 22 scheduled languages, a requirement now given legal force by the Digital Personal Data Protection Rules 2025. Those Rules, notified in November 2025, impose consent, erasure and breach-notification obligations that apply across the full data lifecycle rather than at the point of collection alone. The framework gives agricultural universities, ICAR institutes and state departments a basis for assigning data management roles and planning curation training. Because it rests on published literature and policy documents, it has not yet been tested against current institutional practice.