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<p>Rice leaf disease classification employing machine learning approaches is an important and ongoing research area due to its wider production and consumption across the globe. Although it is evident that Convolutional Neural Networks (CNNs) have brought about a significant paradigm change in the domain of image recognition, especially when detecting agricultural diseases. However, its ‘black box’&#xa0;nature prevents humans from understanding and interpreting its work for decision making. In this context, it is preferable to make an effort to render it explainable. Hence, in this paper, the working of the CNN model is explained through three important techniques such as layer-wise relevance propagation (LRP), SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). A modified version of LRP is used to explain the workings of each CNN layer by propagating the relevance score backwards over the network. The model prediction is analyzed and explained using SHAP. The region of importance that impacts the model to predict the specific disease is explained using LIME. This work provides a clear vision of the functioning and interpretation of the CNN model for different classifications of rice leaf diseases. Experimental results found 96.5% accuracy, which show the efficacy of the proposed CNN model.</p>

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Interpretable and Explainable Convolutional Neural Network for Rice Leaf Disease Detection

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Swati Lipsa,
  • Ranjan Kumar Dash,
  • Debasis Gountia

摘要

Rice leaf disease classification employing machine learning approaches is an important and ongoing research area due to its wider production and consumption across the globe. Although it is evident that Convolutional Neural Networks (CNNs) have brought about a significant paradigm change in the domain of image recognition, especially when detecting agricultural diseases. However, its ‘black box’ nature prevents humans from understanding and interpreting its work for decision making. In this context, it is preferable to make an effort to render it explainable. Hence, in this paper, the working of the CNN model is explained through three important techniques such as layer-wise relevance propagation (LRP), SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). A modified version of LRP is used to explain the workings of each CNN layer by propagating the relevance score backwards over the network. The model prediction is analyzed and explained using SHAP. The region of importance that impacts the model to predict the specific disease is explained using LIME. This work provides a clear vision of the functioning and interpretation of the CNN model for different classifications of rice leaf diseases. Experimental results found 96.5% accuracy, which show the efficacy of the proposed CNN model.