基于改进YOLOv5s轻量化识别模型的智能采茶装置设计OA
Design of Intelligent Tea-picking Device Based on Improved YOLOv5s Lightweight Recognition Model
针对采茶行业面临的劳动力短缺和采摘效率低下问题,提出了一种基于改进YOLOv5s的轻量化茶芽识别算法,并设计了智能采茶装置.围绕目标识别算法、三维精确定位和采摘收集一体化等核心问题,并结合三维空间定位技术、机械结构与流体力学创新,构建了一套高效、精准的智能采茶系统.首先,针对茶芽检测的实时性和轻量化需求,提出了一种融合GhostNet轻量级网络、CARAFE上采样算子和EIOU损失函数的改进YOLOv5s模型;其次,针对茶叶采摘点的精确定位需求,提出了一种基于双目深度相机与图像处理的三维空间定位算法;最后,设计了一种基于XYZ三轴运动平台收集方式的采茶装置,并进行采摘试验,结果表明该模式可行、有效,茶芽识别成功率为(86.6±3.4)%,识别成功后的采摘式成功率为(90±2)%.研究可为智能化采茶的设计思路、技术方向和元件选择等提供参考.
Aiming at the problems of labor shortage and low picking efficiency in the tea-picking industry,a lightweight tea bud recognition algorithm based on the improved YOLOv5s was proposed,an intelligent tea-picking device was de-signed.Focusing on core issues including target recognition algorithms,3D precise positioning,and integrated picking and collection,and constructed a highly efficient and accurate intelligent tea-picking system by integrating 3D spatial positioning technology,mechanical structure innovations,and fluid mechanics advancements.Firstly,in view of the real-time and lightweight requirements for tea bud detection,an improved YOLOv5s model was proposed,which integrat-ed the GhostNet lightweight network,CARAFE up-sampling operator,and EIOU loss function.Secondly,to address the need for accurate positioning of tea-picking points,a 3D spatial positioning algorithm based on a binocular depth camera and image processing was proposed.Finally,a tea-picking device based on an XYZ three-axis motion platform,featu-ring integrated picking and collection functions was designed.Experimental tests were conducted on the tea-picking de-vice,and the results demonstrated that the proposed device was feasible and effective.The success rate of tea bud recoghition was(86.6±3.4)%,the success rate of picking after successful identification was(90±2)%.This paper provides an alternative approach regarding the concept,technical directions,and component selection for intelligent tea picking.
Xu Jiahe;Gao Junran;Yu Fusheng;Zhang Xiao;Yu Zikun;Zhang Tongjia
School of Mechanical and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,ChinaSchool of Mechanical and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,ChinaSchool of Mechanical and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,ChinaSchool of Mechanical and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,ChinaSchool of Mechanical and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,ChinaSchool of Mechanical and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,China
信息技术与安全科学
采茶装置机器视觉YOLOv5s轻量化采摘点定位深度学习
tea-picking devicemachine visionYOLOv5s lightweightpicking-point positioningdeep learning
《农机化研究》 2026 (3)
54-61,8
山东省自然科学基金项目(ZR2024QE374)
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