Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
This work addresses early disease detection for rice farming, an incremental improvement in plant pathology using existing methods on new data.
The paper tackled the problem of fine-grained rice leaf disease detection by proposing a dual-loss framework combining Center Loss and ArcFace Loss, achieving accuracies of 99.6%, 99.2%, and 99.2% on three backbone architectures.
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.