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<p>Wheat is a critical staple crop that plays a key role in global food security, particularly in developing countries like Ethiopia. However, fluctuations in wheat yields, often driven by factors like disease, pose significant challenges to agricultural productivity in these regions. Brown rust, stem rust, and yellow rust are the major types of rust diseases that generally cause massive reductions in crop quality and yield. An automated deep learning technique is needed for the classification of these diseases in agricultural fields. In this study, different CNN Architectures were compared to classify wheat rust diseases from wheat image data. The data is split into training, testing, and validation with ratios of 65:20:15, respectively. MobileNetV3, DenseNet121, ResNet50, and EfficientNetB0 were utilized to classify wheat rust disease. Among all of these, MobileNetV3 outperformed these models with an accuracy of 97.7%. Hence, due to this performance, MobileNetV3 became the best model for wheat crop disease classification and management.</p>

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Comparative evaluation of CNN architectures for wheat rust diseases classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

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  • Andargie Geteneh Tegegne,
  • Yelekal Mulualem Walle,
  • Melaku Bitew Haile,
  • Getachew Tadesse Yehulu,
  • Surafel Tsegaye Yohannes

摘要

Wheat is a critical staple crop that plays a key role in global food security, particularly in developing countries like Ethiopia. However, fluctuations in wheat yields, often driven by factors like disease, pose significant challenges to agricultural productivity in these regions. Brown rust, stem rust, and yellow rust are the major types of rust diseases that generally cause massive reductions in crop quality and yield. An automated deep learning technique is needed for the classification of these diseases in agricultural fields. In this study, different CNN Architectures were compared to classify wheat rust diseases from wheat image data. The data is split into training, testing, and validation with ratios of 65:20:15, respectively. MobileNetV3, DenseNet121, ResNet50, and EfficientNetB0 were utilized to classify wheat rust disease. Among all of these, MobileNetV3 outperformed these models with an accuracy of 97.7%. Hence, due to this performance, MobileNetV3 became the best model for wheat crop disease classification and management.