基于机器视觉的穴盘幼苗分级移栽系统设计与试验

Design and Experiment of Selecting and Transplanting System of Plug Seedlings Based on Machine Vision

针对穴盘幼苗工厂化生产中人工分级移栽劳动强度较大的问题,提出了基于机器视觉和图像处理自动分级方法,设计了一套穴盘幼苗分级移栽系统。首先,运用图像分割、模板匹配等方法提取穴盘幼苗叶面积、地径、株高3个特征,并以此训练SVM自动分级模型;然后,开发了基于Opencv3.6、Qt5的软件,实现了穴盘幼苗的实时在线分级;最后,使用穴盘幼苗分级移栽样机对7~10天的番茄幼苗进行自动分级移栽试验。结果表明:在平均移栽速度为9.6株/min时,移栽成功率为96.88%,分级正确率为95.83%,可以连续稳定工作,有效解决人工费时费力问题。

Aiming at the needs of automatic grading and transplanting of plug seedlings in factory production,this paper designs a set of plug seedling quality classification system based on machine vision and image processing.Firstly,the hardware system of conveying mechanism,control mechanism,transmission mechanism,actuator and machine vision system is designed.Secondly,the image processing method based on template matching is used to find plug seedlings in the front view and extract their plant height and ground diameter.And,the image segmentation method based on color information is used to find plug seedlings in the top view and extract their leaves.The height,ground diameter and leaf area of plug tray seedlings are used as feature parameters to train the SVM grading model.A polynomial kernel function SVM classifier is used to automatically classify plug tray seedlings.We have developed system control and analysis software based on Opencv3.6,Python3.7 and Qt5,which can visually edit key parameters.Based on the research and development of the plug tray seedling grading transplanting prototype,we conducted automatic grading and transplanting experiments on tomato seedlings of 7-10 days.The test results show that when the average transplanting speed is 9.6 plants/min,the transplanting success rate is 96.88%,and the classification correct rate is 95.83%.Theoretically,using 4 robots for transplanting can achieve a transplanting speed of 38 plants/min,which can work continuously and steadily,effectively solve the labor-consuming and labor-intensive problem,and improve the accuracy of seedling transplanting.The advantages of automatic grading and transplanting are more obvious for long working hours.