Abstract

Yield prediction is critical for planning agricultural production. In this study, wheat yield was predicted using climate, soil and satellite-based vegetation indices for the Konya Plain covering 2012–2023. Random forest, XGBoost, support vector regression and multilayer perceptron models were compared. XGBoost gave the best result with a coefficient of determination of 0.89 and a root mean square error of 212 kg/ha. SHAP analysis revealed that April–May precipitation and NDVI values were the most decisive variables for yield.

Keywords:yield predictionmachine learningXGBoostNDVIprecision agriculture

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Ethics Approval
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How to Cite

Aydoğdu, Z., & Kılıç, F. N. (2024). Comparison of Machine Learning Methods for Wheat Yield Prediction: The Case of the Konya Plain. International Journal of Science and Technology Research, 6(1), 12–29. https://doi.org/10.99999/ubtad.2024.2
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CC BY 4.0

© 2024 Zeynep Aydoğdu, Fatma Nur Kılıç. This article is distributed under the terms of the CC BY 4.0 license, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. License text