  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.
