使用无人机低空遥感和优化的YOLO11模型精确检测茶叶病.
Yaojun Zhang1, Guiling Wu1, Jianbo Shen2,3
1School of Information Engineering, Xinyang Agriculture and Forestry University, Xinyang, Henan, China.
PloS one
|February 18, 2026
概括
这项研究介绍了FCHE-YOLO,这是一个改进的轻量级模型,用于使用无人机检测茶叶疾病. 它实现了更高的精度和更快的速度,使其成为无人机 (UAV) 边缘部署的理想选择.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 茶叶疾病显著影响作物产量和质量.
- 现有的智能检测方法与复杂的背景,有限的数据和高的计算成本作斗争.
- 需要精确,高效和边缘部署的解决方案来实时监测茶叶病.
研究的目的:
- 开发一个改进的轻量级检测模型,FCHE-YOLO,用于准确和快速识别茶叶疾病.
- 为了提高复杂环境中的检测准确性和稳定性.
- 为了使资源有限的无人机 (UAV) 边缘设备能够有效地部署.
主要方法:
- 建议基于YOLO11的FCHE-YOLO模型,并进行了三个关键优化.
- 引入轻量级的骨干模块FC_C3K2以减少计算和增强强性.
- 构建了高效的特征融合结构HSFPN,用于多层次信息集成.
- 设计的检测头 有效的头部与集团卷积和注意力机制,以提高准确性.
主要成果:
- 与YOLO11.11相比,FCHE-YOLO的平均精度 (mAP) 从94.1%提高到98.1%.
- 推断速度增加了9.0% (从43.3 FPS增加到47.5 FPS),满足了实时需求.
- 计算复杂性显著降低:FLOP降低了34.3% (6.4G至4.2G),参数降低了38.9% (2.59M至1.46M).
结论:
- FCHE-YOLO提供了卓越的检测准确度,并减少了茶叶疾病的错过检测.
- 该模型的轻量级设计和效率使其非常适合在无人机上进行边缘部署.
- 开发的模型是实用的实时监测茶叶疾病使用无人机基于低海拔遥感.
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