用迁移学习+集成方法，精准识别可可果病害
Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling
- 用预训练模型做特征提取，三种集成策略联合分类
- 袋装法准确率达100%，显著优于提升和堆叠方法
- 适合农业智能诊断，尤其对病害早期识别有帮助
本研究提出一种基于集成学习的可可果病害分类方法，融合迁移学习与三种集成策略：袋装法、提升法和堆叠法。采用VGG16、VGG19、ResNet50、ResNet101、InceptionV3和Xception等预训练卷积神经网络作为基学习器，用于识别黑斑腐烂、果蛀虫和健康三类病害。构建了包含6,000张可可果图像的均衡数据集，并通过数据增强应对光照、朝向和病害严重程度的变化。使用准确率、精确率、召回率和F1分数评估性能。实验结果表明，袋装法在测试集上达到100%准确率，优于提升法（97%）和堆叠法（92%）。研究证实，结合迁移学习与集成方法可有效提升模型泛化能力与可靠性，为精准农业和自动化作物病害管理提供可行路径。
原文摘要 · Abstract (English)
This study presents an ensemble-based approach for cocoa pod disease classification by integrating transfer learning with three ensemble learning strategies: Bagging, Boosting, and Stacking. Pre-trained convolutional neural networks, including VGG16, VGG19, ResNet50, ResNet101, InceptionV3, and Xception, were fine-tuned and employed as base learners to detect three disease categories: Black Pod Rot, Pod Borer, and Healthy. A balanced dataset of 6,000 cocoa pod images was curated and augmented to ensure robustness against variations in lighting, orientation, and disease severity. The performance of each ensemble method was evaluated using accuracy, precision, recall, and F1-score. Experimental results show that Bagging consistently achieved superior classification performance with a test accuracy of 100%, outperforming Boosting (97%) and Stacking (92%). The findings confirm that combining transfer learning with ensemble techniques improves model generalization and reliability, making it a promising direction for precision agriculture and automated crop disease management.
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