轻量级模型融合可见光与植被指数，精准识别辣椒病害并可解释。
XMACNet: An Explainable Lightweight Attention based CNN with Multi Modal Fusion for Chili Disease Classification
- 结合自注意力与多模态融合，提升病害特征提取能力。
- 在1.2万张图像上达到高准确率，优于ResNet等基线模型。
- 支持可视化解释，适合边缘设备部署于真实农田场景。
基于成像的植物病害分类是精准农业中的关键任务。本文提出XMACNet，一种新型轻量级卷积神经网络，通过自注意力机制与可见光图像及植被指数（NDVI、NPCI、MCARI）的多模态融合，实现辣椒病害检测。模型采用EfficientNetV2S骨干网络，嵌入自注意力模块与融合分支。我们构建了包含12,000张辣椒叶片图像的新数据集，涵盖六类（五种病害及健康），并通过StyleGAN进行合成增强以缓解数据稀缺问题。在该数据集上训练后，XMACNet在准确率、F1分数和AUC上均优于ResNet-50、MobileNetV2及Swin Transformer变体。尤为重要的是，模型具备可解释性：采用Grad-CAM++与SHAP可视化并量化模型对病害特征的关注。其紧凑结构与快速推理使其适用于实际农业生产中的边缘部署。
原文摘要 · Abstract (English)
Plant disease classification via imaging is a critical task in precision agriculture. We propose XMACNet, a novel light-weight Convolutional Neural Network (CNN) that integrates self-attention and multi-modal fusion of visible imagery and vegetation indices for chili disease detection. XMACNet uses an EfficientNetV2S backbone enhanced by a self-attention module and a fusion branch that processes both RGB images and computed vegetation index maps (NDVI, NPCI, MCARI). We curated a new dataset of 12,000 chili leaf images across six classes (five disease types plus healthy), augmented synthetically via StyleGAN to mitigate data scarcity. Trained on this dataset, XMACNet achieves high accuracy, F1-score, and AUC, outperforming baseline models such as ResNet-50, MobileNetV2, and a Swin Transformer variant. Crucially, XMACNet is explainable: we use Grad-CAM++ and SHAP to visualize and quantify the models focus on disease features. The models compact size and fast inference make it suitable for edge deployment in real-world farming scenarios.
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