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

EasyChair Preprint 2759

Abstract

The 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 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 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.

Keyphrases: Artificial Neural Network, Artificial Neural Networks, Artificial neural network model, Crop yield prediction, Gray Wolf Optimization, Gray Wolf Optimizer, Gray wolf, Hybrid Machine Learning Method, Imperialist Competitive Algorithm, ann ica, artificial neural network gray wolf, crop yield, electrical engineering obuda university, food security, hybrid machine learning, machine learning method, neural network, neural network gray wolf optimizer, neural network imperialist competitive algorithm, obuda university budapest, potato sugar beet, regia technical faculty obuda, technical faculty obuda university, wheat barley potato

@booklet{EasyChair:2759,
  author    = {Saeed Nosratabadi and Karoly Szell and Bertalan Beszedes and Imre Felde and Sina Ardabili and Amir Mosavi},
  title     = {Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction},
  howpublished = {EasyChair Preprint 2759},
  year      = {EasyChair, 2020}}