Abstract
The objective of this study is to develop an AI-based fruit monitoring system to address the issue of image background noise when automatically counting tomatoes grown in greenhouse horticulture. The system consists of a scanning device and a rail-guided vehicle. This system creates a panoramic image of the crop canopy and automatically counts the number of fruits using deep learning-based fruit detection model (Mask R-CNN). A focused illumination unit on our scanning device creates a light-dark contrast between the near and far sides of the camera, removing unnecessary objects from the background of the images. We applied our system to detect tomato fruits grown in an experimental greenhouse and successfully achieved good performance (AP50 = 0.95).
