资源高效的棉花网络:棉花疾病和害虫分类的轻量级深度学习框架
Zhengle Wang1, Heng-Wei Zhang2, Ying-Qiang Dai3
1College of Information and Electrical Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China.
Plants (Basel, Switzerland)
|July 12, 2025
概括
一个新的轻量级模型,RF-Cott-Net,使用开源数据集准确检测棉花疾病和害虫. 这项技术支持作物管理和育种研究,在边缘设备上提供高效的实时性能.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 由于疾病和害虫,棉花种植面临着巨大的产量和质量损失.
- 准确和快速的诊断对于有效的疾病管理和育种计划至关重要.
研究的目的:
- 开发一种轻量级,高效的深度学习模型,用于诊断棉花疾病和害虫.
- 引入一个开源数据集 (CCDPHD-11),用于培训和评估棉花病检测模型.
主要方法:
- 拟议的RF-Cott-Net模型,使用MobileViTv2骨干与早期退出和量子化意识训练 (QAT).
- 开发和使用CCDPHD-11数据集,包括11种棉花疾病类别.
- 根据准确性,F1分数,精度和回忆来评估模型性能.
主要成果:
- 在CCDPHD-11数据集上,RF-Cott-Net实现了高精度 (98.4%),F1得分 (98.4%),精度 (98.5%) 和回忆 (98.3%).
- 该模型的效率高,参数为4.9M,FLOP为310M,推理时间为3.8ms,存储空间为4.8MB.
- 已证明适合在农业边缘设备上部署,用于实时检测.
结论:
- RF-Cott-Net提供了一个高度准确和高效的解决方案,用于自动在现场检测棉花疾病和害虫.
- 该模型的轻量化性质和性能支持在农业中的实际应用,帮助作物管理和遗传研究.
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