Recognition and Detection of Greenhouse Tomatoes in Complex Environment

Abstract

In the complex environment of greenhouses, it is important to provide the picking robot with accurate information. For this purpose, this paper improves the recognition and detection method based on you only look once v5 (YOLO v5). Firstly, adding data enhancement boosts the network generalizability. On the input end, the k-means clustering (KMC) was utilized to obtain more suitable anchors, aiming to increase detection accuracy. Secondly, it enhanced multi-scale feature extraction by improving the spatial pyramid pooling (SPP). Finally, non-maximum suppression (NMS) was optimized to improve the accuracy of the network. Experimental results show that the improved YOLO v5 achieved a mean average precision (mAP) of 97.3%, a recall of 90.5%, and an F1-score of 92.0%, while the original YOLO v5 had a mAP of 95.9% and a recall of 85.6%; the improved YOLO v5 took 57ms to identify and detect each image. The recognition accuracy and speed of the improved YOLOv5 are much better than those of faster region-based convolutional neural network (Faster R-CNN) and YOLO v3. After that, the improved network was applied to identify and detect images take in unstructured environments with different illumination, branch/leave occlusions, and overlapping fruits. The results show that the improved network has a good robustness, providing stable and reliable information for the operation of tomato picking robots.