湖南农业大学学报（自然科学版）2024，Vol.50Issue(5)：112-118,7.DOI:10.13331/j.cnki.jhau.2024.05.016
机采茶叶嫩芽的图像采集与识别
Image acquisition and recognition of machine-harvested tea buds
摘要
Abstract
To enhance the intelligence level of mechanical tea harvesting,the author designed a tea harvesting experimental platform consisting of a support frame,arc-shaped harvesting blade,blade screw lifting plate,4 rollers,2 drive motors,controller,and battery pack.Using YOLOv5s 6.0 as the baseline model,several modifications were implemented:the backbone network was replaced with MobilenetV3;a CBAM attention module was integrated before the detection layer;the lightweight universal upsampling operator CARAFE was adopted to substitute the nearest neighbor interpolation method.Furthermore,by incorporating trade-off functions and enhancing the CIOU loss function,a novel mathematical model YOLOv5s+was developed for tea leaf detection.Subsequently,tea bud images taken at different heights(10,20,30,40,50 cm)and angles(15°,30°,45°,60°,75°,90°)were used as samples to test their impact on network recognition accuracy.The results demonstrated that optimal model performance was achieved when images were acquired at a 20 cm vertical distance from the tea tree canopy with a 45° shooting angle.Using the image set captured under these parameters for ablation experiments,YOLOv5s+achieved mean average precision and recall rates of 0.935 and 0.912 respectively for tea bud recognition,showing improvements of 2.97%and 2.82%compared to YOLOv5s.关键词
茶叶机采/YOLOv5s/茶叶嫩芽识别/图像采集/图像识别Key words
machine harvesting for tea buds/YOLOv5s/tea bud recognition/image acquisition/image recognition分类
农业科技引用本文复制引用
俞龙,黄浩宜,周波,黄楚斌,唐劲驰,胡春筠..机采茶叶嫩芽的图像采集与识别[J].湖南农业大学学报（自然科学版）,2024,50(5):112-118,7.基金项目
广东省重点领域研发计划项目(2023B0202120001) （2023B0202120001）
广东省农业科学院农业优势产业学科团队建设项目(202125TD) （202125TD）
