轻量Vision Transformer+运行时增强，高效识别芒果叶病
MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classification
- 用轻量ViT+自注意力捕捉病害间复杂模式
- 在MangoLeafBD数据集达99.43%准确率，模型更小更快
- 适合部署在低算力设备，农业病害检测利器
保障食品安全对公共健康、经济稳定和全球供应链至关重要。芒果作为南亚多国主要农产品，因多种病害导致高额经济损失，影响整个产业链。尽管已有深度学习方法用于芒果叶病分类，但缺乏在低算力设备上高效运行的解决方案。本文提出一种基于轻量Vision Transformer的分类框架，采用自注意力机制，在保持极低计算开销的同时实现领先性能。方法通过全局注意力捕捉病害间的细微差异，并引入运行时增强提升泛化能力。在MangoLeafBD数据集上的评估显示，准确率达99.43%，在模型尺寸、参数量和FLOPs方面均优于现有方法。
原文摘要 · Abstract (English)
Ensuring food safety is critical due to its profound impact on public health, economic stability, and global supply chains. Cultivation of Mango, a major agricultural product in several South Asian countries, faces high financial losses due to different diseases, affecting various aspects of the entire supply chain. While deep learning-based methods have been explored for mango leaf disease classification, there remains a gap in designing solutions that are computationally efficient and compatible with low-end devices. In this work, we propose a lightweight Vision Transformer-based pipeline with a self-attention mechanism to classify mango leaf diseases, achieving state-of-the-art performance with minimal computational overhead. Our approach leverages global attention to capture intricate patterns among disease types and incorporates runtime augmentation for enhanced performance. Evaluation on the MangoLeafBD dataset demonstrates a 99.43% accuracy, outperforming existing methods in terms of model size, parameter count, and FLOPs count.
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