Abstract
Healthy seedlings transplanting is an important process in the production of vegetables and economic crops, and the transplanting quality directly affects crop yield. Automatic seedlings transplanting can improve the transplanting efficiency of seedlings. A physical prototype of potted seedling automatic transplanting with a conveyor and a transplanting end-effector was developed in the previous study. This work proposed an automatic transplanting method of healthy potted seedlings, which mainly included a detection part of seedlings growth status and a visual servo control part with the purpose of automatic transplanting seedlings high-efficiently. The seedlings and the tray cell were simultaneously detected for identifying healthy seedlings, damaged seedlings and empty cells using a machine vision algorithm when the tray was moving on the conveyor line. The visual servo model was applied to enable the collaborative operation of the machine vision and the end-effector for determining the position and attitude of grasping seedlings. The experimental results showed that the accuracy rates of the identification of empty tray cells, healthy seedling and unhealthy seedling were 96.42%, 98.77% and 89.95%, respectively. Under the successful identification of the healthy seedling, the accuracy rate of grasping seedling was 96.38%. It indicated that the proposed method can effectively transplant seedlings. Keywords: seedling recognition, automatic transplanting, computer vision, transplanting system DOI: 10.25165/j.ijabe.20211406.6638 Citation: Jin X, Tang L M, Ji J T, Wang C L, Wang S S. Potential analysis of an automatic transplanting method for healthy potted seedlings using computer vision. Int J Agric & Biol Eng, 2021; 14(6): 162–168.
Highlights
Seedling transplanting is an important process in the production of economic crops such as vegetables and cotton
The seedlings and the tray cell were simultaneously detected for identifying healthy seedlings, damaged seedlings and empty cells using a machine vision algorithm when the tray was moving on the conveyor line
The detection results of the empty tray cell, the healthy seedlings and unhealthy seedlings are shown in Figures 4e and 4f, respectively
Summary
Seedling transplanting is an important process in the production of economic crops such as vegetables and cotton. Few studies discussed the integration of seedling detection and end-effector for determining position and attitude of seedling grasp. Methods based on machine vision technology and hyperspectral technology were used in seedling quality detection. Few studies were reported about the transplanting methods that could adjust the position and attitude of end-effector to grasp seedlings based on detecting the growth state of potted seedlings. This study proposed an intelligent transplanting method of greenhouse potted seedlings with the aim of detecting seedling quality, identifying the empty cell of the tray, and determining the position and attitude of grasping seedlings using machine vision technology. (1) A vision algorithm called the detection algorithm of Grid Coordinate-Based Overlapping Image (GCOI) was developed for simultaneously identifying healthy seedlings, damaged seedlings and empty tray cells by using charge-coupled device (CCD) images analysis.
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