基于改进的YOLOv10-TQ和DeepSort的复杂环境中对卷心菜移植状态检测和操作质量评估的研究
Yiqun Wang1, Mengchen Liu1, Yue Geng1
1College of Automation, Beijing Information Science and Technology University, Beijing, China.
Frontiers in plant science
|December 29, 2025
概括
这项研究介绍了一种人工智能驱动的系统,用于监测卷心菜移植质量. 该方法可以准确地检测和计数开放田地的苗木,提高农业效率和产量预测.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 种植品质对植物的生存和产量产生重大影响.
- 机械化移植面临着手工检查效率和传统算法准确性的挑战.
- 开放的田野环境对目前的苗木检测和跟踪方法存在困难.
研究的目的:
- 开发一种强大的检测和跟踪方法,以在现实条件下评估白菜移植质量.
- 为了提高识别各种移植状态 (正常,土壤埋藏,裸根) 的准确性和稳定性.
- 实现精确可靠的种植苗计数,以改善农业管理.
主要方法:
- 改进的YOLOv10-TQ网络与三重注意力和QFocal Loss交叉被用于增强的苗木状态检测.
- 一个轻量级的MobileViT功能提取器被集成到DeepSort算法中,以改进目标跟踪.
- 为了准确的苗木计数和身份脱重复,实施了交叉计数策略.
主要成果:
- 拟议的方法在幼苗状态检测方面实现了86.3%的平均平均精度 (mAP).
- 在一个定制的卷心菜移植数据集上记录了97.8%的平均计数精度.
- 该系统表现出比传统方法更高的检测准确度,跟踪稳定性和计数精度.
结论:
- 开发的人工智能驱动的方法在白菜移植的智能质量评估方面取得了重大进展.
- 该方法为农业机械系统的数据驱动决策提供了坚实的技术基础.
- 创建了一个可视化系统来监控移植状态,促进精准农业.
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