International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Crop Yield Prediction At District Level In India Using Machine Learning

Author(s) Mr. Raj Guru, Mr. Sahil Jana, Ms. Diya Choudhary, Ms. Chhanak Dixit, Dr. Mahadev Kumar
Country India
Abstract The Indian economy relies heavily on agriculture. Computing crop yield at a finely differentiated spatial level is a complicated task because of varied and regional diversity of India and the interaction of different factors affecting the crop yield. Using the historical agricultural data, a machine learning framework for crop yield prediction is proposed at a district level in this paper. The dataset consists of multi-year agricultural data generated by considering attributes of crop, area, season and geographic data. Different machine learning methods refer to The yield variation is modeled using Random Forest, Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). In addition to that, Root Mean Squared Error, Mean Absolute Error and coefficient of determination (R2)
regression metrics are implemented. A temporal data-splitting approach is used for training and testing. The model uses a past year’s data for training and a future year’s data for testing. Tree-based models are able to perform well in modelling nonlinear data among all models. Additional methods of explainable
artificial intelligence are applied to explanation analysis. An analysis was done of the Feature Importance which showed that features like crop, area, season, etc. are important.
Keywords Machine Learning, XGBoost, Random Forest, Multi-Layer Perceptron, Explainable AI, SHAP, Agricultural Data Analysis.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-23
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E-ISSN 2582-2160
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