Real-Time Vegetables Recognition System based on Deep Learning Network for Agricultural Robots

Abstract

Automation is a major challenge in the application of agricultural robots. Therefore, in order for agricultural robots to automatically classify and detect vegetables, an effective identification system should be established, which will increase production efficiency. In this paper, we propose a real-time vegetables recognition system based on deep learning network to detect and classify vegetables. On the basis of the large vegetable dataset obtained by our pinking robot, our goal is to find the appropriate framework for pinking mission with high accuracy and fast speed. Therefore, we select several one-stage and two-stage networks as alternative detectors including Faster R-CNN, SSD, RFB Net, YOLOv2, and YOLOv3. With the promotion of data augmentation, we demonstrate the recognition network based on YOLOv3 combing the depth meta-framework and feature extractor achieve the well performance in terms of both accuracy and speed by reducing the number of false positives in training phase. Comparative experiments indicate that our recognition system can effectively identify seven different kinds of vegetables (the mean AP can reach 87.89 %, and the detection speed is up to 38FPS), which would handle with real-time detection task and improve pinking capability of our agricultural robot.