强大的实时草成熟度检测使用无人机安装的深度学习用于精密农业
Rajmeet Singh1, Appaso M Gadade2, Irfan Hussain3
1Department of Mechanical Engineering, Khalifa University, Abu Dhabi, 127788, United Arab Emirates.
BMC plant biology
|October 14, 2025
概括
这项研究介绍了一种自主四旋翼无人机系统与YOLOv9-GLEAN实时草监测在温室. 该系统提供精确的检测和计数,推进精确农业.
科学领域:
- 农业机器人农业机器人
- 计算机视觉在农业中的应用
- 精准农业技术 精准农业技术
背景情况:
- 传统的温室监测是劳动密集型的,缺乏可扩展性.
- 精准农业需要高效的实时监控解决方案.
- 自主系统对于先进的作物管理至关重要.
研究的目的:
- 开发一个自主四旋翼无人机系统用于草植物监测.
- 为温室环境实施一个强大的草检测模型.
- 通过自动化作物评估来加强精准农业.
主要方法:
- 使用了一架配备机载摄像头的四旋翼无人机.
- 开发并测试了一种用于成熟草检测的YOLOv9-GLEAN算法.
- 实现了用于无人机导航的混合轨迹跟踪控制器 (PID+LQR).
主要成果:
- YOLOv9-GLEAN模型在检测成熟的草方面取得了很高的准确性.
- 混合控制器展示了卓越的跟踪性能.
- 综合系统在模拟和现实世界温室环境中都有效.
结论:
- 深度学习模型,特别是YOLOv9-GLEAN,可以通过四旋翼无人机快速,精确,自动检测成熟的草.
- 这项技术远远超过了传统的手动检查方法.
- 该系统支持知情决策,以尽量减少作物损失和优化产量.
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