通过CCRDNet提取中央作物行,用于农业中通用的行内导航
Hao Zheng1, Qiang Wang1,2
1Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Frontiers in plant science
|February 18, 2026
概括
这项研究引入了一种新的深度学习方法,用于作物排列检测,简化农业机械导航. 该方法准确地提取中央作物行,使实时操作和零射击对新环境的概括成为可能.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 现有的深度学习方法用于作物行检测需要广泛的定制和预处理.
- 人类操作员可以通过遵循中央作物行来直观地导航,这是当前自动化系统缺乏的能力.
研究的目的:
- 开发一种新的深度学习策略,用于作为导航线的直接中央作物排列提取.
- 为了简化农业机械的导航管道,并在各种条件下实现强大的性能.
主要方法:
- 引入了三类注释方案 (背景,植被,中部作物行) 与一致的作物行宽度.
- 开发了CCRDNet (中央作物行检测网络) 来预测中央行位置,并使用最小正方形来适应导航线.
- 在8种作物类型的多样化数据集 (7,367张图像) 的有限子集 (400张图像) 上训练模型.
主要成果:
- 实现了95.57%的导航线提取精度,平均角度误差为1.13°.
- CCRDNet具有轻量级 (0.033M参数) 并实现高推理速度 (RTX 3060上的86.76 FPS,Jetson Orin NX上的48.78 FPS).
- 证明了对未见的环境和作物类型的零射击通用化,培训数据有限.
结论:
- 拟议的方法通过直接提取中央作物行来简化农业导航.
- CCRDNet提供了一个实用,高效和可通用的解决方案,用于实时的农业机械指导.
- 该方法满足了自主农业操作的实时需求.
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