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

ArXi:2603.25006v1 Announce Type: cross Early detection of rice leaf diseases is critical, as rice is a staple crop ing a substantial share of the world's population. Timely identification of these diseases enables 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.