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YOLO-RSTS:一种精确的细分模型,用于检测树上的防腐剂和刺激剂喷区域

Jincan Zhu^1,2,3, Yu Feng^2,3, Fengming Liu^2,3

¹College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China.

Frontiers in plant science

|January 23, 2026

概括

这项研究介绍了YOLO-RSTS,这是一个改进的计算机视觉模型,用于精确的树喷. 它通过精确细分喷区域来提高乳产量和种植园管理,优于现有的方法.

科学领域:

计算机视觉计算机视觉
农业技术农业技术
机器学习机器学习

背景情况:

传统的手动喷用于树乳产量是低效的,不适合大型种植园.
现有的细分模型与复杂的树皮纹理和可变的照明作斗争,导致不准确的喷边界.

研究的目的:

开发一个改进的细分模型,YOLO-RSTS,用于准确识别树上防腐剂和兴奋剂喷区域.
提高种植园自主喷系统的效率和准确性.

主要方法:

拟议的YOLO-RSTS模型基于YOLOv12n-Seg框架,使用了新的CrossScaleDSC,CG-Attention和C2f-DSC模块.
将RFCAConv和DWConv纳入骨干和头部,以改善空间多样性和上下文表示.
在自建数据集上训练和评估模型.

主要成果:

与YOLOv12n相比,YOLO-RSTS取得了显著的改进:精度为+6.3%,mAP0.50为+6.3%,回忆为+8.1%.
与YOLOv12n.n.相比,参数数量减少了14.5%,与YOLOv12n.n.相比,参数数量减少了14.5%.
优于YOLOv13n的表现,获得了7.5%的mAP0.50和9.2%的F1分数.

结论:

在种植园中,YOLO-RSTS为基于视觉的自主喷提供了有效和高效的解决方案.
拟议的模型显示了推动智能种植园管理和提高乳产量的巨大潜力.

关键词:

CG-注意力机制的使用在CrossScaleDSC和C2f-DSC模块中使用.这是一个YOLO-RSTSTSTS.自动化乳种植园管理系统喷防腐剂和乙烯的喷雾剂.

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