轻量级视觉变换模型，让手机也能精准识别植物病害
MobilePlantViT: A Mobile-friendly Hybrid ViT for Generalized Plant Disease Image Classification
- 混合结构设计，兼顾效率与准确率
- 0.69万参数下准确率超99%，优于同类小模型
- 适合资源受限的移动端智能农业应用
植物病害严重威胁全球粮食安全，导致作物减产并破坏农业可持续性。基于AI的自动分类技术展现出巨大潜力，深度学习模型在植物病害识别中表现优异。然而，由于计算需求高、资源受限，将这些模型部署到移动和边缘设备仍面临挑战，亟需轻量且精准的解决方案以实现可及的智能农业系统。为此，我们提出MobilePlantViT，一种专为泛化植物病害图像分类设计的新型混合视觉变换器（Hybrid ViT）架构，在保持高性能的同时优化资源效率。在多个不同规模的植物病害数据集上进行的大量实验表明，该模型具备出色的有效性与强泛化能力，测试准确率范围从80%到超过99%。特别地，仅含0.69万参数的架构，在性能上超越了MobileViTv1和MobileViTv2中最小型号，尽管后者参数更多。结果证明该方法在可持续、资源高效的真实世界智能农业系统中具有广阔前景。所有代码将开源至GitHub：https://github.com/moshiurtonmoy/MobilePlantViT
原文摘要 · Abstract (English)
Plant diseases significantly threaten global food security by reducing crop yields and undermining agricultural sustainability. AI-driven automated classification has emerged as a promising solution, with deep learning models demonstrating impressive performance in plant disease identification. However, deploying these models on mobile and edge devices remains challenging due to high computational demands and resource constraints, highlighting the need for lightweight, accurate solutions for accessible smart agriculture systems. To address this, we propose MobilePlantViT, a novel hybrid Vision Transformer (ViT) architecture designed for generalized plant disease classification, which optimizes resource efficiency while maintaining high performance. Extensive experiments across diverse plant disease datasets of varying scales show our model's effectiveness and strong generalizability, achieving test accuracies ranging from 80% to over 99%. Notably, with only 0.69 million parameters, our architecture outperforms the smallest versions of MobileViTv1 and MobileViTv2, despite their higher parameter counts. These results underscore the potential of our approach for real-world, AI-powered automated plant disease classification in sustainable and resource-efficient smart agriculture systems. All codes will be available in the GitHub repository: https://github.com/moshiurtonmoy/MobilePlantViT
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