用注意力机制提升植物病叶识别准确率与可解释性
Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN
- 在VGG16中每层加入CBAM注意力模块，增强特征提取
- 在5个数据集上最高达98.87%准确率，泛化能力强
- 通过多种可视化方法实现模型决策过程透明化
植物病害对全球粮食安全构成重大威胁，亟需高精度且可解释的病害检测方法。本研究提出一种可解释的注意力引导卷积神经网络（CBAM-VGG16），在每个卷积阶段引入卷积块注意力模块（CBAM），以增强特征提取与病害定位能力。模型在五个不同植物病害数据集上训练，性能优于近期方法，最高准确率达98.87%，并展现出良好泛化能力。通过CBAM注意力图、Grad-CAM、Grad-CAM++和层相关性传播（LRP）等方法进行综合评估与可解释性分析，验证了该方法的有效性。本研究推动了可解释人工智能在农业诊断中的应用，为智慧农业提供了透明可靠的系统支持。代码已开源：https://github.com/BS0111/PlantAttentionCBAM。
原文摘要 · Abstract (English)
Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, the model enhances feature extraction and disease localization. Trained on five diverse plant disease datasets, our approach outperforms recent techniques, achieving high accuracy (up to 98.87%) and demonstrating robust generalization. Here, we show the effectiveness of our method through comprehensive evaluation and interpretability analysis using CBAM attention maps, Grad-CAM, Grad-CAM++, and Layer-wise Relevance Propagation (LRP). This study advances the application of explainable AI in agricultural diagnostics, offering a transparent and reliable system for smart farming. The code of our proposed work is available at https://github.com/BS0111/PlantAttentionCBAM.
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