Intelligent Crop Recommendation System Using Machine Learning for Jharkhand Agriculture

Author(s)	Mr. Abhinav Pathak, Dr. Sanjeev Kumar, Prof. Dr. Sandeep Kumar
Country	India
Abstract	Agriculture is a cornerstone of India's economy, and Data Driven Farming presents a significant opportunity to boost national productivity. This is particularly relevant for Jharkhand, a state where 80% of the rural population relies on agriculture yet remains one of India's poorest (as of 2020). The region's adherence to traditional, non-data-driven farming methods has resulted in stagnant crop yields and hindered economic progress. To address this, XGBoost-Based Supervised Learning Approach is proposed to improve yield predictions. The model was trained on identical historical data, including temperature, rainfall, irrigation, and soil nutrient composition (N, P, K). The study found that the proposed method delivered the most accurate crop yield predictions, identifying it as the optimal tool to help modernize and improve agricultural outcomes in Jharkhand.
Keywords	Digital / Data Driven Farming, Supervised Learning algorithms, Multiple Regression, Neural Networks, Decision Tree Regressor, Random Forest, XGBoost, etc
Field	Engineering
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-13
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.66285

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