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持续采摘路径规划基于轻量级花花冠状在现场的识别

Baojian Ma¹, Zhenghao Wu², Yun Ge²

¹Department of Mechanical and Electrical Engineering, Xinjiang Institute of Technology, Aksu 843100, China.

Biomimetics (Basel, Switzerland)

|October 28, 2025

概括

本研究介绍了MPD-YOLO,用于精确识别花冠状和在复杂环境中有效规划路径. 轻量级模型提高了自动收获的准确性和速度,克服了照明和遮蔽等挑战.

科学领域:

农业机器人农业机器人
计算机视觉计算机视觉
机器学习机器学习

背景情况:

自动收获需要精确的对象识别和高效的路径规划.
复杂的自然条件,如照明和遮蔽,带来了重大挑战.

研究的目的:

开发一个集成的轻量级框架,用于花冠状的识别和路径规划.
为了提高在非结构化的环境中自动化花收获的准确性和效率.

主要方法:

开发了MPD-YOLO,这是一个优化的YOLOv11n模型,具有多尺度信息增强模块 (MSEE).
实施结构化修剪和知识蒸,用于模型压缩和准确度恢复.
利用适应性殖民地算法与动态参数调整用于路径规划.

主要成果:

MPD-YOLO实现了高精度 (P: 89.8%,mAP@0.5: 95.1%) 和稳定性,回忆率增加了6.8%.
模型尺寸缩小至2.1 MB (39.6%的原始),计算负载为3.2 GFLOPs.
使用自适应性殖民地算法,路径规划时间减少到2.2秒 (68.6%的加快速度).

结论:

综合框架显著提高了自动花收获的感知精度和操作效率.
轻量级和强大的MPD-YOLO模型为现实世界农业应用提供了可行的解决方案.
这项研究为在具有挑战性的农业环境中持续自动化操作提供了强大的技术支持.

关键词:

自动收获的自动化收获不断的挑选不断的挑选标识标识标识标识标识轻量级的轻量级的轻量级的轻量级的玛丽戈尔德的黄金是一种黄金.路径规划路径规划路径规划

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