Image-based recognition of green perilla leaves using a deep neural network for robotic harvest support

This paper describes a method of recognizing green perilla leaves using a deep neural network for harvest support in greenhouse horticulture. We are developing a robot for harvest support, which automates the selection and bundling process. In order to manipulate green perilla leaves correctly, the robot needs to precisely estimate their geometrical parameters such as width, height, and orientation. It also needs to detect leaves with anomalies. Therefore, we develop an image-based leaf recognition method, adopting deep neural network (DNN) techniques. To reduce computation time, we design a network for executing multiple tasks simultaneously, namely, segmentation and classification. We also developed an annotated dataset using conventional image processing techniques. Experimental results show the efficiency and effectiveness of the proposed method.