基于改进YOLOv8的蝴蝶兰组培苗视觉伺服种植平台设计与试验  

Design and experiment of the visual servo planting platform forphalaenopsis tissue-cultured seedlings using improved YOLOv8

作　　者：苑朝 马嘉宁 张盼浩 赵明雪 王家豪 王静娴 徐大伟 YUAN Chao;MA Jianing;ZHANG Panhao;ZHAO Mingxue;WANG Jiahao;WANG Jingxian;XU Dawei(Department of Automation,North China Electric Power University,Baoding 071003,China;Hebei Province Power Generation Process Simulation and Optimization Control Technology Innovation Center(North China Electric Power University),Baoding 071003,China;Hebei Baisha Tobacco Co.,Ltd.Baoding Cigarette Factory,Baoding 071000,China)

机构地区：[1]华北电力大学自动化系,保定071003 [2]河北省发电过程仿真与优化控制技术创新中心(华北电力大学),保定071003 [3]河北白沙烟草有限责任公司保定卷烟厂,保定071000

出　　处：《农业工程学报》2024年第20期138-146,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金联合基金项目重点支持项目(U21A20486);河北省高等学校科学技术研究项目(QN2024171);中央高校基本科研业务费专项资金(2022MS100)。

摘　　要：为降低蝴蝶兰组织培养快速繁育的人力成本,该研究提出了一种基于视觉伺服机械臂的自动化组培苗种植平台,以完成流水线上蝴蝶兰组培苗的自动夹取与种植。平台主要由视觉检测系统和机械臂种植系统组成,在视觉检测系统中,通过将AKConv与DSConv模块引入YOLOv8算法形成AKDS_YOLOv8检测算法,提高系统对组培苗识别的准确率;在机械臂种植系统中,基于模糊算法实现机械臂的伺服控制,使机械臂末端能顺利完成对传送带上组培苗的追踪及夹取。试验结果表明,相较于原YOLOv8,AKDS_YOLOv8对组培苗根部的识别准确率、召回率、真实框与检测框交并比值取50%时的平均检测精度分别提高了8.6、10.7、7.4个百分点;实现了机械臂末端工具对移动组培苗的追踪、抓取与种植,种植成功率达到82.5%。该种植平台能够实现蝴蝶兰组培苗的自动化种植,可为蝴蝶兰快速繁育过程的自动化提供一定参考。Phalaenopsis has been widely popular ornamental plants,due to its unique appearance and excellent market prospects.The commonly used breeding for phalaenopsis is tissue culture at the same time.However,this task involves high repetition and labor intensity.It is still challenging to increase the production of butterfly orchids.This study aims to improve the yield of tissue culture for the labor-saving in phalaenopsis production.A visual servo platform was proposed with the robotic arm gripping.The picking and planting of tissue-cultured seedlings were realized on the conveyor belt.The visual robotic arm was constructed as a 6-degree-of-freedom robotic arm with eyes on the hand,according to the uncalibrated visual servo control.Firstly,the images were captured from the camera.Image detection was implemented to obtain the image coordinates of the roots,stems,and leaves of phalaenopsis tissue-cultured seedlings.Then,the image data was analyzed to determine the angle between the tissue-cultured seedlings and the end clamp of the robotic arm,as well as the gripping point.According to this angle,the end posture of the robotic arm was obtained during gripping.Afterward,the difference in picking point coordinates was calculated between the current and expected image for the tissue-cultured seedlings.The obtained data was then fed into the fuzzy as an input.The acceleration value was derived to control the speed at the end of the robotic arm from the input quantity.The end gripper of the robotic arm was used to track the tissue-cultured seedlings during planting.The acceleration control was utilized to remain relatively stationary in the planting experiment.The tracking speed was also maintained to plant in the given and fixed culture medium.The accuracy of the original YOLOv8 was improved to identify the roots,particularly for the diverse root morphology of Phalaenopsis tissue-cultured seedlings.AKConv and DSConv modules were introduced to extract the root features,resulting in an improved AKDS_YOLOv8.Compared with the o

关 键 词：蝴蝶兰 组培苗 YOLOv8 AKConv DSConv 模糊控制 

分 类 号：S126[农业科学—农业基础科学]