Crop Yield Prediction Using Supervised Machine Learning Algorithms
DOI:
https://doi.org/10.59994/ajbtme.2025.1.43Keywords:
Crop Yield Prediction, Supervised Machine Learning, Data PreprocessingAbstract
This paper discusses different supervised machine learning models in order to come up with a predictive model of crop yield, which utilizes the soil and environmental parameters. The data is a creation of the Kaggle in the project of Samrudha hackathon and is intended to support AI-based applications to smart and sustainable farming. We have done strict data preprocessing, which includes the elimination of outliers, the processes of duplicate and missing data. Various regression algorithms, such as K-Nearest Neighbors (KNN), Linear Regression, Ridge and Lasso Regression, Support Vector Regression (SVR), Decision Trees, and Random Forests were used and tested. R 2 and Mean Squared Error (MSE) were used as performance measurements. The Random Forest Regressor was the best performing model out of all the models tested with a test R 2= 0.9394 and a test MSE= 4.0840. This indicates the strength and capability of generalizing of ensemble approaches of agricultural yield forecasting activities. The originality of this study lies in its systematic and rigorous comparison of multiple supervised machine learning models for crop yield prediction using carefully preprocessed soil and environmental data. It further contributes by demonstrating the superior generalization capability of ensemble methods, particularly Random Forests, in supporting accurate and sustainable smart farming applications.
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