Low-damage transplanting method for leafy vegetable seedlings based on machine vision

Abstract

To solve the high damage rate in the seedling transplantation of horticultural facilities, a low-damage transplanting method of leafy vegetable seedlings based on machine vision and image processing was proposed. An Intel Realsense D415 camera was used to obtain the side image of single-row seedlings. Then, image processing based on Python-Opencv was performed to obtain the seedlings’ height and extreme edge points. The pixel coordinates in the RGB image were then obtained, and the depth image was aligned to the RGB image to acquire the depth information of the corresponding extreme points. Then, the path planning of the end effector was carried out according to the coordinated information to realise the low-damage transplantation of seedlings. The calibration accuracy of the low-damage transplanting method for seedling edge extreme points was verified experimentally. Under the experimental conditions, the calibration success rate was 98%, and the deviation ratio was within 2%. The seedling transplantation experiment compared (a) the low-damage transplanting method with (b) the fixed-path and (c) the vertical-horizon motion transplanting method. The success rates of the three methods were 94.90%, 88.89%, and 93.52%, and the average single transplanting time was 4.895 s, 4.907 s and 5.627 s. It was proved that the low-damage transplanting method for leafy vegetable seedlings based on machine vision could significantly reduce the seedling damage rate and increase the success rate of seedling transplantation.