[2603.25006] Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25006 (cs) [Submitted on 26 Mar 2026] Title:Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning Authors:Md. Rokon Mia, Rakib Hossain Sajib, Abdullah Al Noman, Abir Ahmed, B M Taslimul Haque View a PDF of the paper titled Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning, by Md. Rokon Mia and 4 other authors View PDF HTML (experimental) Abstract: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 substant...