用集成模型提升柠檬叶病识别准确率至99.27%。
An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification
- 融合InceptionV3与MobileNetV2，通过集成学习提升鲁棒性。
- 在1354张图像上实现99.27%准确率，验证集表现稳定。
- 适合农业病害检测、边缘设备部署及高可靠性场景。
早期发现植物病害对作物和农民至关重要。病害会降低果实产量与品质，使植株更易受其他胁迫影响。该柠檬叶病数据集包含1354张图像，分为9类：1类为健康叶片，其余8类为不同病害。经全面预处理后，数据集按70%训练、15%测试、15%验证划分。采用两个预训练模型（InceptionV3与MobileNetV2），通过集成技术融合，显著提升模型稳健性。集成模型达到99.27%的准确率。引入对抗训练以增强模型在噪声数据下的预测能力。通过Grad-CAM可视化，定位叶片中对分类关键的区域，验证了模型预测的可信度。
原文摘要 · Abstract (English)
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.
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