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.

翻译：植物病害诊断对农户的管理决策至关重要，因为植物病害常导致作物减产和品质下降。葡萄叶片病害检测对于保障作物健康生长和提高农业生产率具有重要意义。本研究所用植物病害数据集包含总计9,032张四类别葡萄叶片图像，其中三类为病害叶片，另一类为健康叶片。经过严格预处理后，数据集按70%训练集、20%验证集、10%测试集划分，并部署了InceptionV3和Xception两种预训练模型。Xception模型取得了96.23%的准确率，表现显著优于InceptionV3。研究采用对抗训练提升模型鲁棒性，同时增强可解释性。集成Grad-CAM方法以验证叶片病害区域。最终基于Streamlit框架部署了具备热力图可视化及置信度预测功能的网络应用，实现了鲁棒的葡萄叶片病害分类。