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

arXiv: https://arxiv.org/abs/2603.25006 · HTML: https://mlsanity.com/paper/2603.25006/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [A Deep Learning Framework to Reconstruct Face under Mask](https://mlsanity.com/paper/2203.12482/) (2022)
- [Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning](https://mlsanity.com/paper/2203.06721/) (2022)

## Abstract (arXiv)

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.