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提高竹子颜色和斑点的识别使用新的YOLO模型

Yunlong Zhang¹, Tangjie Nie², Qingping Zeng²

¹College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China.

Plants (Basel, Switzerland)

|August 14, 2025

概括

一个新的深度学习模型,YOLOv8-BS,准确地检测竹子射颜色和斑点. 这项技术有助于对竹子物种进行分类,并支持可持续农业.

科学领域:

农业科学农业科学
计算机科学计算机科学
遗传学是一个遗传学.

背景情况:

竹子射的颜色和斑点是物种识别,经济估值和遗传研究的关键表型标记.
准确和高效的分析这些特征的方法对于竹子生殖质评价和保护工作至关重要.

研究的目的:

开发和评估一个深度学习模型,YOLOv8-BS,用于精确检测竹子射上的颜色和点图案.
将YOLOv8-BS的性能与已建立的对象检测模型进行比较,以在Chimonobambusa使用*中进行表型特征分析.

主要方法:

使用了来自中国金福山的 *Chimonobambusa utilis* 射击的数据集.
应用数据增强技术,包括翻译,翻转和对比度调整,以增强训练数据集.
实现并对YOLOv8-BS模型与YOLOv7,YOLOv5,YOLOX和更快的R-CNN进行基准测试,用于颜色和点检测.

主要成果:

与基准模型相比,YOLOv8-BS在检测颜色和斑点图案方面表现出卓越的性能.
对于色彩检测,YOLOv8-BS的精度为85.9%,回忆率为83.4%,F1得分为84.6%,平均精度为86.8%.
对于点检,YOLOv8-BS的精度达到了90.1%,回忆率达到了92.5%,F1得分达到了91.1%,AP达到了96.1%.

结论:

YOLOv8-BS模型提供了一个高度准确和强大的解决方案,用于竹子生长的自动化表型分析.
这种深度学习方法促进了精确的生殖质评价,遗传多样性研究,并支持通过精密农业开发可持续的竹子产业.
未来的工作可能将重点放在提高细粒度品种区分和实时应用能力的模型上.

关键词:

在YOLOv8-BS模型中,竹子的芽生长在竹子上这是分类分类的分类.深度学习是一种深度学习.现象型现象型是一种现象型.

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