Authors: Ambuj Kumar Misra
Abstract: Accurate crop yield prediction is essential for food security, agricultural planning, and policy formulation. This research paper presents a comprehensive analysis of district-level crop yield prediction using government open data and artificial intelligence techniques [1]. The study leverages publicly available datasets from agricultural ministries, meteorological agencies, and remote sensing sources to develop predictive models utilizing machine learning and deep learning approaches. Our analysis demonstrates that ensemble methods combining multiple algorithms achieve superior accuracy compared to individual models, with R² values exceeding 0.85 on validation datasets [2]. The proposed framework integrates soil characteristics, weather patterns, crop management practices, and historical yield data to create robust prediction systems deployable across different geographical regions [3]. Results indicate that incorporating remote sensing data and temporal patterns significantly improves model performance [4]. This research contributes to the growing body of knowledge on precision agriculture and provides practical guidelines for government agencies and farmers to optimize yield forecasting systems.
DOI: https://doi.org/10.5281/zenodo.19479317
