Real-time Detection of Green Onion Branch Position on Edge Devices

Abstract

In recent years, a shortage of farm workers in green onion production has become a serious problem. Therefore, there is a need to further mechanize preparation. Current machinery cannot remove all unwanted leaves at once, requiring subsequent manual removal. To reduce this secondary processing, it is effective to align the nozzle with the uppermost branch position. It is necessary to recognize the branch position of each green onion and feed the plant into the machine with the branch position aligned. Detecting the branch position by image recognition and automatic alignment of the nozzle for preparation could improve the accuracy of preparation. In this paper, we propose a method for detecting the branch position by extracting a particular oblique line at the branch. The method is designed for implementation on low-power edge devices and uses a by lightweight edge detection algorithm based on image processing. On a Raspberry Pi 3, it achieved a correct detection rate of 90.6% and a processing time of 455 ms. This result shows that our method is effective for detecting the branch position and may be applied to real-world use.