Artificial Neural Networks Outperform Traditional Methods in Predicting Wheat Production in Iraq

Strategic Insights -

Researchers from the University of Information Technology and Communications have conducted a study comparing the predictive abilities of multiple linear regression (MLR) and artificial neural networks (ANN) in estimating wheat production in Iraq. The study found that ANN outperformed MLR, producing more accurate estimates with lower error levels. This breakthrough has significant implications for Iraqi agricultural planning and food security management.

Key Takeaways:

  • The study utilized wheat output data from 2007 to 2021 and evaluated the performance of both MLR and ANN models using Mean Absolute Percent Error (MAPE), Mean Squared Error (MSE), and Mean Absolute Error (MAE).
  • Artificial neural networks were found to be more accurate in predicting wheat production in Iraq, with lower error levels compared to multiple linear regression.
  • The study concluded that until 2025, artificial neural networks provided superior tools for Iraqi agricultural planning and food security management.
  • The research was published in the Iraqi Journal for Computers and Informatics, Volume 51, Issue 1, pp. 130-139, 2024.
  • The study's findings have the potential to improve crop yields, reduce food insecurity, and enhance economic growth in Iraq.
  • The AI-based system developed in the study can be adapted for use in other agricultural settings, promoting food security and sustainable development.

Statistics:

  • 2007-2021: The period over which wheat output data was collected and used in the study.
  • 51(1):130-139: The volume and issue number of the Iraqi Journal for Computers and Informatics where the study was published.
  • 2025: The year by which artificial neural networks are expected to continue providing superior tools for Iraqi agricultural planning and food security management.
  • 10.25195/ijci.v51i1.572: The Digital Object Identifier (DOI) citation for the study.
  • 0.5%-1.5: The range of Mean Absolute Percent Error (MAPE) values used to evaluate the performance of the ANN model.

Sources:

  • VerticalNews. Data on Artificial Neural Networks Published by a Researcher at University of Information Technology and Communications (Study of Factors Affecting the Production of Strategic Crops in Iraq Using Artificial Neural Networks). VerticalNews, 2025 JUL 21.
  • NewsRx. Data on Artificial Neural Networks Published by a Researcher at University of Information Technology and Communications (Study of Factors Affecting the Production of Strategic Crops in Iraq Using Artificial Neural Networks). Journal of Engineering. July 21, 2025; p 354.
  • Iraqi Journal for Computers and Informatics. Study of Factors Affecting the Production of Strategic Crops in Iraq Using Artificial Neural Networks. Iraqi Journal for Computers and Informatics, 2024,51(1):130-139.