Early detection of plant diseases is crucial to plants and for the farmers. Plant diseases reduce fruit yield and quality, and plants are more susceptible to other stresses when they are infected. The lemon leaf disease dataset contains 1354 images. The dataset has 9 classes. Among the 9 classes only one class is for healthy leaf, and the other 8 classes are leaf diseases. The dataset was split into training (70%), testing (15%) and validation (15%) sets after comprehensive preprocessing. Two pretrained models (InceptionV3 and MobileNetV2) were applied and then combined these models using an ensemble technique to boost robustness. Ensemble models showed a promising performance of 99.27% accuracy. Adversarial Training is applied to improve models' ability and ensure reliable predictions under noisy data. Grad-CAM visualization highlights the important regions of leaf images that validate the model prediction with confidence level.

翻译：植物病害的早期检测对植株及农户至关重要。病害会降低果实产量与品质，且受感染的植株更易遭受其他胁迫。本研究采用的柠檬叶病害数据集包含1354张图像，涵盖9个类别，其中仅1类为健康叶片，其余8类均为叶片病害。经全面预处理后，数据集按70%、15%、15%的比例划分为训练集、测试集与验证集。应用InceptionV3与MobileNetV2两种预训练模型，并通过集成技术融合两种模型以提升鲁棒性。集成模型展现出卓越性能，准确率达99.27%。采用对抗训练增强模型能力，确保其对含噪声数据做出可靠预测。基于Grad-CAM的可视化技术突出显示叶片图像的关键区域，以置信度水平验证模型预测结果的有效性。