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基于YOLOv5的茶叶嫩芽图像识别算法研究

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现有的机器采茶都需要人工辅助进行采茶,且存在老叶、嫩芽一刀切的情况,会损害一部分茶叶,只适用于低端茶叶的采摘.因此,需要研究出一种精准高效的茶叶嫩芽识别方法.针对茶叶嫩芽图像背景复杂的问题,在YOLOv5算法的基础上,对算法进行多角度的改进,实验结果表明,改进算法的模型的mAP提升了 4.1%,Re-call提升了 4.0%,且改进方法减少了漏检情况的发生.
Research on Image Recognition Algorithm of Tea Shoots Based on YOLOv5
The existing machinery for tea picking requires manual assistance for tea picking,and there is a situation where old leaves and tender buds are cut in one piece,which can damage a portion of the tea and is only suitable for picking low end tea.Therefore,it is necessary to develop an accurate and efficient method for identifying tea buds.Aiming at the problem of complex background in tea bud images,this pa-per addresses the issue of complex backgrounds in images of tea bud shoots.Based on the YOLOv5 algo-rithm,the paper implements multifaceted improvements to the algorithm.The experimental results show that the improved model's mean Average Precision(mAP)has increased by 4.1%,and Recall has increased by 4.0%,with the improved method reducing the occurrence of missed detections.

tea pickingYOLOv5 algorithmmachine learningtender bud identification

马志艳、李辉

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湖北工业大学农机工程研究设计院,湖北武汉 430068

茶叶采摘 YOLOv5算法 机器学习 嫩芽识别

国家重点研发计划基金资助项目

2018YFD0701002-03

2024

湖北工业大学学报
湖北工业大学

湖北工业大学学报

CHSSCD
影响因子:0.258
ISSN:1003-4684
年,卷(期):2024.39(1)
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