Robust cabbage recognition and automatic harvesting under environmental changes

Abstract

In Japanese agriculture, labor shortages are becoming increasingly severe due to the lack of farmers and aging. Therefore, extensive research has been conducted on the automation of cabbage harvesting. In automatic cabbage harvesting, cabbage detection is performed using deep learning. In the evening hours, if the backlight enters the camera, cabbage detection is not possible. Moreover, cabbages in the back row that are not to be harvested are detected and targeted for harvest. To solve these problems, we have proposed new recognition methods in this paper. We performed cabbage detection using the lower half of the cabbage and performed cabbage selection using an RGB-D camera. Moreover, sliding-mode control was incorporated to enable automatic harvesting in the soft soil. The experimental results demonstrate the effectiveness of these proposed methods.