Low-productivity agricultural belts in India face the compounding realities of land fragmentation, erratic weather, and heterogeneous soils, which together limit harvest outcomes. Precision agriculture and big data analytics together offer an innovative solution that turns large volumes of heterogeneous data into actionable agronomic intelligence. This study proposes a big-data-driven crop yield prediction (CYP) framework tailored to low-productivity districts in India. The architecture combines soil and climatic analytics with data warehousing, machine learning (four base regressors and voting-based hybrid ensembles), and a preprocessing pipeline designed for compatibility with distributed... More >