Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode

被引:7
|
作者
Wang, Da [1 ]
Su, Rui [1 ]
Xiong, Yanjie [1 ]
Wang, Yuwei [1 ,2 ]
Wang, Weiwei [1 ,2 ]
机构
[1] Anhui Agr Univ, Sch Engn, Hefei 230036, Peoples R China
[2] Anhui Prov Engn Lab Intelligent Agr Machinery & E, Hefei 230036, Peoples R China
关键词
sugarcane; computer vision; precision agriculture; pre-cutting mode; YOLO V5;
D O I
10.3390/s22218430
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
China is the world's third-largest producer of sugarcane, slightly behind Brazil and India. As an important cash crop in China, sugarcane has always been the main source of sugar, the basic strategic material. The planting method of sugarcane used in China is mainly the pre-cutting planting mode. However, there are many problems with this technology, which has a great impact on the planting quality of sugarcane. Aiming at a series of problems, such as low cutting efficiency and poor quality in the pre-cutting planting mode of sugarcane, a sugarcane-seed-cutting device was proposed, and a sugarcane-seed-cutting system based on automatic identification technology was designed. The system consists of a sugarcane-cutting platform, a seed-cutting device, a visual inspection system, and a control system. Among them, the visual inspection system adopts the YOLO V5 network model to identify and detect the eustipes of sugarcane, and the seed-cutting device is composed of a self-tensioning conveying mechanism, a reciprocating crank slider transmission mechanism, and a high-speed rotary cutting mechanism so that the cutting device can complete the cutting of sugarcane seeds of different diameters. The test shows that the recognition rate of sugarcane seed cutting is no less than 94.3%, the accuracy rate is between 94.3% and 100%, and the average accuracy is 98.2%. The bud injury rate is no higher than 3.8%, while the average cutting time of a single seed is about 0.7 s, which proves that the cutting system has a high cutting rate, recognition rate, and low injury rate. The findings of this paper have important application values for promoting the development of sugarcane pre-cutting planting mode and sugarcane planting technology.
引用
收藏
页数:21
相关论文
共 50 条
  • [21] Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor
    Elwakeel, Abdallah Elshawadfy
    Nasrat, Loai S.
    Badawy, Mohamed Elshahat
    Elzein, I. M.
    Mahmoud, Mohamed Metwally
    Kitmo
    Hussein, Mahmoud M.
    Hussein, Hany S.
    El-Messery, Tamer M.
    Nyambe, Claude
    Elsayed, Salah
    Ourapi, Manar A.
    PLOS ONE, 2024, 19 (10):
  • [22] Pre-Seed Workshop: Bolstering the entrepreneurial pipeline for high-technology, scalable businesses
    Wilson, Mark W.
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2012, 243
  • [23] The development of an automatic rubber seed sowing system with machine vision assistance
    A. Mohd Mustafah
    S. Khairunniza-Bejo
    Y. Lim
    Journal of Rubber Research, 2022, 25 : 187 - 194
  • [24] Design and Evaluation of a Novel Transversal Double-bud Sugarcane Planter with Seed Pre-cutting
    Zhong, Jia-Qin
    Tao, Li-Min
    Li, Shang-Ping
    Ma, Fang-Lan
    Chen, Yuan-Ling
    SUGAR TECH, 2021, 23 (05) : 1147 - 1156
  • [25] The development of an automatic rubber seed sowing system with machine vision assistance
    Mustafah, A. Mohd
    Khairunniza-Bejo, S.
    Lim, Y.
    JOURNAL OF RUBBER RESEARCH, 2022, 25 (03) : 187 - 194
  • [26] Instantaneous frequency enhanced peak detection for sugarcane seed cutting
    Wei Junfeng
    Tang Weidong
    Wen Chunming
    Huang Longdian
    INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY, 2021, 67 (2-3) : 149 - 158
  • [27] Design and Evaluation of a Novel Transversal Double-bud Sugarcane Planter with Seed Pre-cutting
    Jia-Qin Zhong
    Li-Min Tao
    Shang-Ping Li
    Fang-Lan Ma
    Yuan-Ling Chen
    Sugar Tech, 2021, 23 : 1147 - 1156
  • [28] Ginseng Must Cutting System Based on Machine Vision
    Xue, Dingzhu
    Xiao, Pei
    PROCEEDINGS OF THE 2016 6TH INTERNATIONAL CONFERENCE ON MECHATRONICS, COMPUTER AND EDUCATION INFORMATIONIZATION (MCEI 2016), 2016, 130 : 684 - 688
  • [29] Online Recognition of Small Vegetable Seed Sowing Based on Machine Vision
    Zhang, Weipeng
    Zhao, Bo
    Gao, Shengbo
    Ji, Yuxi
    Zhou, Liming
    Niu, Kang
    Qiu, Zhaomei
    Jin, Xin
    IEEE ACCESS, 2023, 11 : 134331 - 134339
  • [30] Research on Classification Method of Maize Seed Defect Based on Machine Vision
    Huang, Sheng
    Fan, Xiaofei
    Sun, Lei
    Shen, Yanlu
    Suo, Xuesong
    JOURNAL OF SENSORS, 2019, 2019