用微调CNN模型精准识别芒果叶病，准确率达99.33%。
Fine-Tuned CNN-Based Approach for Multi-Class Mango Leaf Disease Detection
- 采用迁移学习微调五种预训练CNN模型，实现多类病害识别
- DenseNet201达99.33%准确率，对剪枝象鼻虫和细菌性溃疡识别最优
- 适合智慧农业中病害快速诊断，尤其适用于相似病害区分
芒果是南亚重要水果作物，但其种植常受叶部病害影响，严重降低产量与品质。本研究基于迁移学习与微调策略，评估了五种预训练卷积神经网络（DenseNet201、InceptionV3、ResNet152V2、SeResNet152、Xception）在八类芒果叶病多分类识别中的表现。通过准确率、精确率、召回率、F1分数及混淆矩阵等标准指标进行评估。结果表明，DenseNet201性能最佳，整体准确率达99.33%，各病类指标稳定，尤其在识别剪枝象鼻虫和细菌性溃疡上表现突出。ResNet152V2与SeResNet152亦表现良好，而InceptionV3与Xception在视觉相似病害（如煤烟病与白粉病）中表现较差。高优模型的训练与验证曲线显示稳定收敛。研究证明微调迁移学习模型可有效实现精准可靠的多类芒果叶病检测，适用于智能农业应用。
原文摘要 · Abstract (English)
Mango is an important fruit crop in South Asia, but its cultivation is frequently hampered by leaf diseases that greatly impact yield and quality. This research examines the performance of five pre-trained convolutional neural networks, DenseNet201, InceptionV3, ResNet152V2, SeResNet152, and Xception, for multi-class identification of mango leaf diseases across eight classes using a transfer learning strategy with fine-tuning. The models were assessed through standard evaluation metrics, such as accuracy, precision, recall, F1-score, and confusion matrices. Among the architectures tested, DenseNet201 delivered the best results, achieving 99.33% accuracy with consistently strong metrics for individual classes, particularly excelling in identifying Cutting Weevil and Bacterial Canker. Moreover, ResNet152V2 and SeResNet152 provided strong outcomes, whereas InceptionV3 and Xception exhibited lower performance in visually similar categories like Sooty Mould and Powdery Mildew. The training and validation plots demonstrated stable convergence for the highest-performing models. The capability of fine-tuned transfer learning models, for precise and dependable multi-class mango leaf disease detection in intelligent agricultural applications.
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