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、识别准确率偏低、环境适应性较差等缺陷，结合大众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相关实验。通过搭建自主移动采茶机器人视觉识别系统，完成了本研究所需数据集的构建工作，该数据集主要涵盖茶叶图像采集、图像增强与样本标注三个核心环节。本研究选取YOLOv5与SSD目标检测算法开展模型训练实验，并对实验数据进行系统性分析。实验结果表明，YOLOv5目标检测算法的平均准确率表现突出。进一步的实验数据分析显示，YOLOv5目标检测算法在茶芽分类识别任务中具备良好的应用效果，可为茶芽分类识别与智能采摘作业提供技术支撑与理论指导。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于茶芽质量分类识别的视觉数据集，主要为自主移动采茶机器人设计，包含单芽、一芽一叶、一芽二叶和一芽三叶等不同生长阶段的茶芽图像。它基于YOLOv5和SSD目标检测算法进行实验，旨在提高茶芽识别的准确性和效率，适用于人工智能和机器学习领域的研究与应用。