轻量CNN模型实现33种作物101类病害的高精度移动端识别
Mobile-Friendly Deep Learning for Plant Disease Detection: A Lightweight CNN Benchmark Across 101 Classes of 33 Crops
- 融合多个数据集构建跨作物病害基准数据集
- EfficientNet-B1达94.7%准确率且适合移动端部署
- 为农业田间快速诊断提供轻量化解决方案
植物病害是全球粮食安全的重大威胁，亟需发展可精准识别的早期检测系统。计算机视觉技术为此提供了潜在解决方案。本文构建了一个面向移动设备的轻量级深度学习方案，能够对33种作物的101类植物病害进行准确分类。通过整合PlantDoc、PlantVillage和PlantWild三个公开数据集，构建了涵盖多作物、多病害的综合性基准数据集。评估了MobileNetV2、MobileNetV3、MobileNetV3-Large以及EfficientNet-B0、B1等多种轻量级模型在资源受限设备上的表现。结果表明，EfficientNet-B1在准确率与计算效率之间取得最佳平衡，达到94.7%的分类准确率，具备良好的实际部署潜力。
原文摘要 · Abstract (English)
Plant diseases are a major threat to food security globally. It is important to develop early detection systems which can accurately detect. The advancement in computer vision techniques has the potential to solve this challenge. We have developed a mobile-friendly solution which can accurately classify 101 plant diseases across 33 crops. We built a comprehensive dataset by combining different datasets, Plant Doc, PlantVillage, and PlantWild, all of which are for the same purpose. We evaluated performance across several lightweight architectures - MobileNetV2, MobileNetV3, MobileNetV3-Large, and EfficientNet-B0, B1 - specifically chosen for their efficiency on resource-constrained devices. The results were promising, with EfficientNet-B1 delivering our best performance at 94.7% classification accuracy. This architecture struck an optimal balance between accuracy and computational efficiency, making it well-suited for real-world deployment on mobile devices.
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