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<p>Egypt and other nations are facing considerable difficulties as a result of the uncertainty and variability of wheat imports from international trade markets. Over the past decade, Egypt has produced between 35% and 50% of its total wheat requirements. This paper presents a new Food Security Intelligence Approach (FSIA) that utilizes Data Mining (DM) and decision support techniques to predict the Food Security Status of Crop (FSSC) through its Food Security Intelligence Framework (FSIF). The objective of this study is to develop a FSIF to classify the Food Security Status of Wheat (FSSW) and support decision makers with timely informed decisions for solving crop insecurity issues in the future. FSIF employs data mining classification algorithms such as Random Tree (RT), Random Forest (RF), and Naïve Bayes (NB) to predict and classify the FSSW in Egypt from 2015 to 2021. It extracts the food security patterns of wheat to manage the FSSW and their sufficiency ratios according to the regional wheat production and their demographic consumption, research hypotheses, and decision scenarios in the current and future times. It supports decision-makers with insights and the food security patterns of wheat to mitigate food security issues and rationalize wheat import volumes and costs. The accuracy of the FSIF results for classifying the FSSW was 91–96.8%. The Self-Sufficiency Ratio of Wheat (SSRW) from 2015 to 2021 was 49.11%, 47.7%, 34.55%, 35.45%, 40.28%, 41.36%, and 47%. While using the FSIF, SSRW became 72.7%, 69.1%, 60.66%, 58.94%, 59.35, 62%, and 66.13%, respectively.</p>

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Framework to Predict the Food Security Status of Wheat in Egypt Based on Data Mining Approach

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Mohamed M. Reda Ali,
  • Maryam Hazman,
  • Mohamed H. Khafagy,
  • Rasha M. Badry,
  • Mostafa Thabet

摘要

Egypt and other nations are facing considerable difficulties as a result of the uncertainty and variability of wheat imports from international trade markets. Over the past decade, Egypt has produced between 35% and 50% of its total wheat requirements. This paper presents a new Food Security Intelligence Approach (FSIA) that utilizes Data Mining (DM) and decision support techniques to predict the Food Security Status of Crop (FSSC) through its Food Security Intelligence Framework (FSIF). The objective of this study is to develop a FSIF to classify the Food Security Status of Wheat (FSSW) and support decision makers with timely informed decisions for solving crop insecurity issues in the future. FSIF employs data mining classification algorithms such as Random Tree (RT), Random Forest (RF), and Naïve Bayes (NB) to predict and classify the FSSW in Egypt from 2015 to 2021. It extracts the food security patterns of wheat to manage the FSSW and their sufficiency ratios according to the regional wheat production and their demographic consumption, research hypotheses, and decision scenarios in the current and future times. It supports decision-makers with insights and the food security patterns of wheat to mitigate food security issues and rationalize wheat import volumes and costs. The accuracy of the FSIF results for classifying the FSSW was 91–96.8%. The Self-Sufficiency Ratio of Wheat (SSRW) from 2015 to 2021 was 49.11%, 47.7%, 34.55%, 35.45%, 40.28%, 41.36%, and 47%. While using the FSIF, SSRW became 72.7%, 69.1%, 60.66%, 58.94%, 59.35, 62%, and 66.13%, respectively.