Hybrid Machine Learning Models for Crop Yield Prediction

Amir Mosavi; Bertalan Beszedes; Felde Imre; Karoly Szell; Saeed Nosratabadi; Sina Ardabili

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Hybrid Machine Learning Models for Crop Yield Prediction

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arxiv 2005.04155 v1 pith:CHATWQYT submitted 2020-03-08 cs.NE cs.LGstat.ML

Hybrid Machine Learning Models for Crop Yield Prediction

Saeed Nosratabadi , Felde Imre , Karoly Szell , Sina Ardabili , Bertalan Beszedes , Amir Mosavi This is my paper

classification cs.NE cs.LGstat.ML

keywords croppredictionyieldmodelsann-gwoann-icaartificialfood

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Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of the artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction are evaluated. According to the results, ANN-GWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.

Hybrid Machine Learning Models for Crop Yield Prediction

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