DESIGN AND EXPERIMENTATION OF A MACHINE VISION-BASED QUALITY INSPECTION SYSTEM FOR GREEN ONION SEEDING

In response to the inefficiency and low accuracy issues of traditional detection algorithms in detecting the tray seeding process of green onions, this paper proposes a machine vision-based quality inspection system for green onion seeding. Considering the color characteristics of green onion seeds and the substrate soil, the original RGB images are converted into HSV images. The HSV color filtering algorithm is employed to separate green onion seeds from complex soil backgrounds. Image noise is removed using erosion-dilation operations and small-area methods. The projection method is utilized to determine the detection area of the tray and the position of the holes. Information about connected regions and their convex hulls is extracted, and eight feature parameters including perimeter, area, shape factor, perimeter ratio, area ratio, shape factor ratio, concave defect distance ratio, and error variance are used to establish a BP neural network for single and adherent seed classification. A concave point segmentation algorithm is used to separate adherent green onion seeds and count the number of green onion seeds in each hole to obtain seeding quality parameters of the seeder. Experimental results show that the average relative error of the system qualification rate is 2.24%, with maximum and minimum relative errors of 3.22% and 1.10%, respectively. The average absolute errors of the reseeding rate and void rate are 1.31% and 0.71%, respectively. The absolute error of the average number of particles is 0.025 particles, and the average processing time per image is 0.91 s. The research results provide reference data for precision seeding operations of green onion seeders.