Abstract
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.
Abstract (translated)
芒果是南亚地区重要的水果作物，但其种植经常受到叶病的影响，这些疾病严重影响了产量和质量。这项研究评估了五种预训练的卷积神经网络（CNN）——DenseNet201、InceptionV3、ResNet152V2、SeResNet152 和 Xception，在使用迁移学习策略进行微调的情况下，这五种模型在八个类别的芒果叶病多分类识别中的表现。通过标准评估指标如准确率、精确度、召回率、F1值和混淆矩阵对这些模型进行了性能评估。 在所有测试的架构中，DenseNet201的表现最佳，在各分类上均取得了99.33%的准确性，并且在识别割叶甲（Cutting Weevil）和细菌性溃疡病（Bacterial Canker）方面尤为突出。此外，ResNet152V2 和 SeResNet152 也提供了强大的结果，而 InceptionV3 和 Xception 在视觉上相似类别如煤污病（Sooty Mould）和白粉病（Powdery Mildew）的识别中表现较差。 训练和验证图表明了最佳模型在收敛过程中的稳定性。这项研究表明，微调后的迁移学习模型具有在智能农业应用中进行精确且可靠的多分类芒果叶病检测的能力。
URL
https://arxiv.org/abs/2510.05326
