Study of Factors Affecting the Production of Strategic Crops in Iraq Using Artificial Neural Networks
DOI:
https://doi.org/10.25195/ijci.v51i1.572Keywords:
Wheat production; multiple linear regression; artificial neural networksAbstract
Developed for financial and developmental planning, predictive models work on statistical techniques and artificial intelligence approaches. This project aims to evaluate and contrast Multiple Linear Regression MLR and Artificial Neural Networks ANN in terms of their predictive ability in Iraq's wheat production estimation. The study makes use of wheat output data from 2007 to 2021. Evaluating Mean Absolute Percent Error MAPE alongside Mean Squared Error MSE and Mean Absolute Error MAE enabled two prediction accuracy measures to appraise the performance of both models. Artificial neural networks were found to outperform multiple linear regression since on agricultural data evaluations they produced more exact estimates with lower error levels. Until 2025, artificial neural networks provided superior tools for Iraqi agricultural planning and food security management and consequently became the chosen approach to forecast wheat yields.
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