Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction

Saeed, Nosratabadi and Széll, Károly and Beszédes, Bertalan and Felde, Imre and Ardabili, Sina and Mosavi, Amirhosein (2020) Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction. In: The 2020 RIVF International Conference on Computing & Communication Technologies (RIVF). IEEE, New York, pp. 137-141. ISBN 9781728153773

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Abstract

Prediction of crops 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 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.

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