Modeling Crop Yield Variability through Machine Learning
DOI:
https://doi.org/10.59890/ijir.v4i2.142Keywords:
Crop Yield Prediction, Yield Variability Modeling, Machine Leaning in Agriculture, Soil Properties and NutrientsAbstract
Crop yield variability is a vital issue for farmers, policymakers, and researchers. This study investigate the application of machine learning (ML) techniques to design and predict crop yield variability. We exploit a dataset containing historical climate, soil, and management factors to train and evaluate several ML models, including random forest, support vector machines, and neural networks. Our results show that ML models can effectively capture complex relationships between input features and crop yield, outperforming traditional linear regression models. The best-performing model, a random forest regressor, achieves a mean absolute error (MAE) of 10.2% and R-squared value of 0.85. We identify key factors influencing crop yield variability, including temperature, precipitation, and soil organic carbon content. Our findings demonstrate the potential of ML for improving crop yield prediction and informing decision-making in precision agriculture
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Copyright (c) 2026 Madumere Smart Onyemaechi, Uzoma Peter Ozioma, Ugo Chima, Agada Bob Chile, Odoemene. O Ijeoma, Ihim Kingsley
This work is licensed under a Creative Commons Attribution 4.0 International License.
