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<p>Apple farming plays a&#xa0;significant role in agriculture, serving as an essential source of livelihood for farmers. However, cedar apple rust, apple scab, and black rot are common apple leaf diseases severely affecting apple yield. Early detection of these diseases is crucial for preserving quality and productivity. Researchers have used deep learning models to improve disease classification, they but lack lightweight architecture and transparency, operating as black box systems. Leveraging the power of lightweight models and explainable artificial intelligence (XAI) addresses these challenges by developing transparent methods based on convolutional neural networks (CNNs). This study proposes an enhanced version of MobileNetV2, incorporating a&#xa0;multi-branched architecture to improve feature map representation for classification tasks. The proposed model achieved 99.18% accuracy on the benchmark plant village dataset, surpassing existing studies. The results also integrated local interpretable model-agnostic explanations (LIME) to emphasize the role of individual features in the model’s predictions. The proposed model has a&#xa0;lightweight structure, which ensures its suitability for IoT-based real-time agricultural applications.</p>

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Explainable AI Meets MobileNetV2: A Multi-Branched Approach for Apple Leaf Disease Identification

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Maddassar Jalal,
  • Umar Farooq,
  • Amandeep Kaur

摘要

Apple farming plays a significant role in agriculture, serving as an essential source of livelihood for farmers. However, cedar apple rust, apple scab, and black rot are common apple leaf diseases severely affecting apple yield. Early detection of these diseases is crucial for preserving quality and productivity. Researchers have used deep learning models to improve disease classification, they but lack lightweight architecture and transparency, operating as black box systems. Leveraging the power of lightweight models and explainable artificial intelligence (XAI) addresses these challenges by developing transparent methods based on convolutional neural networks (CNNs). This study proposes an enhanced version of MobileNetV2, incorporating a multi-branched architecture to improve feature map representation for classification tasks. The proposed model achieved 99.18% accuracy on the benchmark plant village dataset, surpassing existing studies. The results also integrated local interpretable model-agnostic explanations (LIME) to emphasize the role of individual features in the model’s predictions. The proposed model has a lightweight structure, which ensures its suitability for IoT-based real-time agricultural applications.