用深度学习模型对比识别木薯病害，EfficientNet-B3表现最佳。
Deep Learning-Based Transfer Learning for Classification of Cassava Disease
- 基于四种CNN模型对比分类木薯病害图像。
- EfficientNet-B3准确率87.7%，F1分数达87.7%。
- 适合农业数字化场景中的病害智能识别应用。
本文对比了四种卷积神经网络架构（EfficientNet-B3、InceptionV3、ResNet50和VGG16）在木薯病害图像分类任务上的性能。图像数据来自一个不平衡的竞赛数据集，采用合适评估指标缓解类别不平衡问题。结果表明，EfficientNet-B3在该任务上达到87.7%的准确率、87.8%的精确率、87.8%的召回率和87.7%的F1分数。研究结果表明，EfficientNet-B3可作为数字农业中支持病害识别的有力工具。
原文摘要 · Abstract (English)
This paper presents a performance comparison among four Convolutional Neural Network architectures (EfficientNet-B3, InceptionV3, ResNet50, and VGG16) for classifying cassava disease images. The images were sourced from an imbalanced dataset from a competition. Appropriate metrics were employed to address class imbalance. The results indicate that EfficientNet-B3 achieved on this task accuracy of 87.7%, precision of 87.8%, revocation of 87.8% and F1-Score of 87.7%. These findings suggest that EfficientNet-B3 could be a valuable tool to support Digital Agriculture.
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