用可解释深度学习提升葡萄叶病识别准确率与鲁棒性
LeafLife: An Explainable Deep Learning Framework with Robustness for Grape Leaf Disease Recognition
- 结合Xception模型与对抗训练提升识别鲁棒性
- 达到96.23%准确率，优于InceptionV3
- 集成Grad-CAM热力图，适合农业从业者使用
植物病害诊断对农民管理决策至关重要，因病害常导致作物减产与品质下降。为保障葡萄丰收与农业增效，葡萄叶病识别尤为重要。数据集包含9,032张图像，涵盖四类：三种病害叶与健康叶。经严格预处理后按70%训练、20%验证、10%测试划分。采用InceptionV3与Xception两个预训练模型，Xception表现更优，准确率达96.23%，显著高于InceptionV3。引入对抗训练增强模型鲁棒性，并通过Grad-CAM实现可解释性分析，确认病害定位。最终基于Streamlit部署交互式网页应用，支持热力图可视化与置信度输出，实现可靠的葡萄叶病分类。
原文摘要 · Abstract (English)
Plant disease diagnosis is essential to farmers' management choices because plant diseases frequently lower crop yield and product quality. For harvests to flourish and agricultural productivity to boost, grape leaf disease detection is important. The plant disease dataset contains grape leaf diseases total of 9,032 images of four classes, among them three classes are leaf diseases, and the other one is healthy leaves. After rigorous pre-processing dataset was split (70% training, 20% validation, 10% testing), and two pre-trained models were deployed: InceptionV3 and Xception. Xception shows a promising result of 96.23% accuracy, which is remarkable than InceptionV3. Adversarial Training is used for robustness, along with more transparency. Grad-CAM is integrated to confirm the leaf disease. Finally deployed a web application using Streamlit with a heatmap visualization and prediction with confidence level for robust grape leaf disease classification.
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