Identification and Localisation Algorithm for Sugarcane Stem Nodes by Combining YOLOv3 and Traditional Methods of Computer Vision

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 21; p. 8266
• Main Authors: Zhou, Deqiang, Zhao, Wenbo, Chen, Yanxiang, Zhang, Qiuju, Deng, Ganran, He, Fengguang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 28.10.2022; MDPI
• Subjects: Affine transformations; Agricultural production; Algorithms; Analysis; automatic cutting; Automation; Computer vision; Deep learning; Design; Equipment and supplies; Identification; improved edge extraction algorithm; Localization; Machine vision; Mechanization; Methods; Nodes; preprocess; R&D; Research & development; Seeds; Stems; Sugar industry; Sugarcane; sugarcane stem nodes; Vision systems; YOLOv3; China
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22218266

Sugarcane stem node identification is the core technology required for the intelligence and mechanization of the sugarcane industry. However, detecting stem nodes quickly and accurately is still a significant challenge. In this paper, in order to solve this problem, a new algorithm combining YOLOv3 and traditional methods of computer vision is proposed, which can improve the identification rate during automated cutting. First, the input image is preprocessed, during which affine transformation is used to correct the posture of the sugarcane and a rotation matrix is established to obtain the region of interest of the sugarcane. Then, a dataset is built to train the YOLOv3 network model and the position of the stem nodes is initially determined using the YOLOv3 model. Finally, the position of the stem nodes is further located accurately. In this step, a new gradient operator is proposed to extract the edge of the image after YOLOv3 recognition. Then, a local threshold determination method is proposed, which is used to binarize the image after edge extraction. Finally, a localization algorithm for stem nodes is designed to accurately determine the number and location of the stem nodes. The experimental results show that the precision rate, recall rate, and harmonic mean of the stem node recognition algorithm in this paper are 99.68%, 100%, and 99.84%, respectively. Compared to the YOLOv3 network, the precision rate and the harmonic mean are improved by 2.28% and 1.13%, respectively. Compared to other methods introduced in this paper, this algorithm has the highest recognition rate.