Chinese Tea Sprout Dataset

On the basis of autonomous mobile tea picking robot, aiming at the shortcomings of traditional tea bud identification methods such as slow speed, low accuracy and poor adaptability, as well as people's demand for high-quality tea, the research and experiment of tea bud quality classification recognition based on YOLOv5 were carried out. Through the construction of the autonomous mobile tea picking robot visual recognition system, the data set was constructed, which mainly included tea image acquisition, enhancement and annotation. YOLOv5 and SSD target detection algorithms were used to conduct model training experiments, and the experimental data was analyzed. The experimental results show that the average accuracy of YOLOv5 target detection algorithm is high.The analysis of experimental data shows that the YOLOv5 target detection algorithm has a good effect on classification identification of tea buds, which can provide technical support and theoretical guidance for classification identification of tea buds and intelligent picking.

本研究以自主移动采茶机器人为依托，针对传统茶芽识别方法存在的速度缓慢、精度偏低、适应性欠佳等缺陷，结合市场对高品质茶叶的需求，开展了基于YOLOv5的茶芽品质分类识别研究与实验。通过搭建自主移动采茶机器人视觉识别系统，完成了本研究数据集的构建工作，该数据集主要涵盖茶叶图像采集、图像增强与标注三大核心环节。分别采用YOLOv5与SSD（Single Shot MultiBox Detector）目标检测算法开展模型训练实验，并对实验数据进行了分析。实验结果表明，YOLOv5目标检测算法的平均精度表现优异。实验数据分析结果显示，YOLOv5目标检测算法在茶芽分类识别任务中效果良好，可为茶芽分类识别与智能采摘提供技术支撑与理论指导。

搜集汇总

背景与挑战

背景概述

该数据集是为自主移动采茶机器人视觉识别系统构建的茶叶芽图像数据集，包含采集、增强和标注的茶叶图像，主要用于茶叶芽质量分类识别研究。数据集支持YOLOv5和SSD等目标检测算法的实验，旨在为茶叶芽智能采摘提供技术支持和理论指导。