用双损失提升稻叶病害细粒度识别准确率
Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
- 结合中心损失与角度损失，增强特征区分能力
- 在三个模型上达99.6%、99.2%、99.2%准确率
- 无需改动结构，适合田间实际部署
早期发现稻叶病害至关重要，因水稻是支撑全球大量人口的主粮作物。及时识别可有效干预，显著降低大规模减产风险。然而，传统深度学习模型多依赖交叉熵损失，常面临类内差异大、类间相似度高的问题，这在植物病理数据集中尤为突出。为此，我们提出一种双损失框架，融合Center Loss与ArcFace Loss，以提升稻叶病害的细粒度分类性能。该方法应用于InceptionNetV3、DenseNet201和EfficientNetB0三种主流骨干网络，在公开的Rice Leaf Dataset上训练，分别取得99.6%、99.2%和99.2%的准确率。结果表明，基于角度边界与中心约束的方法显著增强了特征嵌入的判别力。尤其值得注意的是，该框架无需对模型架构进行重大修改，具备高效性与实用性，适用于农业环境中的实际部署。
原文摘要 · Abstract (English)
Early detection of rice leaf diseases is critical, as rice is a staple crop supporting a substantial share of the world's population. Timely identification of these diseases enables more effective intervention and significantly reduces the risk of large-scale crop losses. However, traditional deep learning models primarily rely on cross entropy loss, which often struggles with high intra-class variance and inter-class similarity, common challenges in plant pathology datasets. To tackle this, we propose a dual-loss framework that combines Center Loss and ArcFace Loss to enhance fine-grained classification of rice leaf diseases. The method is applied into three state-of-the-art backbone architectures: InceptionNetV3, DenseNet201, and EfficientNetB0 trained on the public Rice Leaf Dataset. Our approach achieves significant performance gains, with accuracies of 99.6%, 99.2% and 99.2% respectively. The results demonstrate that angular margin-based and center-based constraints substantially boost the discriminative strength of feature embeddings. In particular, the framework does not require major architectural modifications, making it efficient and practical for real-world deployment in farming environments.
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