Edge recognition and reduced transplantation loss of leafy vegetable seedlings with Intel RealsSense D415 depth camera

Seedling transplantation is the key link in the automation of protected horticulture, and the transplantation quality will directly affect the crop yield. In the previous research, our research group developed a prototype of a low-loss transplantation robot for plug seedlings based on machine vision. On this basis, a method of planning seedling path by integrating seedling edge recognition technology and end effector of transplanting manipulator is proposed to reduce the loss of seedling transplantation. Firstly, the RGB image and depth image of the whole row of seedlings were obtained from the side of the plug seedling with Intel RealSense D415 depth camera. Then, the extreme point coordinates E (Xcb, Yca) of seedling edge were obtained by edge recognition algorithm, and the coordinate value of z-axis was known. Taking point E as the starting point the transplanting manipulator picked up seedlings from the E point in an "L" path, which can reduce the damage of transplanting manipulator to seedling stems and leaves during seedling taking, thereby reducing the transplanting loss. Through three-factor four-level orthogonal test, it was determined that the distance between the depth camera and the plug tray was 600 mm, the height from the horizontal plane was 135 mm, and the light intensity was level 7. Then, a calibration test of extreme points on the edge of plug seedlings was carried out, and the calibration success rate was 98.4%. The deviation of X coordinate was within 5 mm, and the average ratio of deviation was 12.8%. The deviation of Y coordinate was within 4 mm, and the average ratio of deviation was 3%. The quality of image and the accuracy of depth information were the main causes of the deviation. Then, the comparative experiments of routine transplantation group, fixed transplantation group and machine vision transplantation group were carried out. The results showed that compared with the conventional transplantation group, the injury rate of machine vision group decreased by 11.11%, and the average time of single transplantation increased by 0.029 s. Compared with the fixed transplantation group, the injury rate increased by 0.46%, and the average transplantation time decreased by 0.238 s. Because the edge recognition time and the transplantation time intersected and did not interfere, only the time for the first seedling edge recognition was increased in the pipeline operation. Therefore, this method can reduce the damage rate while ensuring the efficiency. This study can provide a reference for reducing the injury rate of leafy vegetable seedlings transplantation.