Approach of Dynamic Tracking and Counting for Obscured Citrus in Smart Orchard Based on Machine Vision

被引:6
|
作者
Feng, Yuliang [1 ,2 ]
Ma, Wei [2 ]
Tan, Yu [1 ]
Yan, Hao [1 ]
Qian, Jianping [3 ]
Tian, Zhiwei [2 ]
Gao, Ang [4 ]
机构
[1] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China
[2] Chinese Acad Agr Sci, Inst Urban Agr, Chengdu 610213, Peoples R China
[3] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
[4] Shandong Agr Univ, Coll Mech & Elect Engn, Tai An 271018, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 03期
关键词
smart orchard; automatic detection; target tracking; dynamic counting;
D O I
10.3390/app14031136
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Automatic counting of packaged wafer die based on machine vision
    Chang, Hsuan-Ting
    Pan, Ren-Jie
    THIRD INTERNATIONAL CONFERENCE ON INFORMATION SECURITY AND INTELLIGENT CONTROL (ISIC 2012), 2012, : 274 - 277
  • [22] Automated counting of palletized slate slabs based on machine vision
    Mato, J. L.
    Alvarez Souto, M.
    Besteiro, R.
    Moledo, J. A.
    39TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2013), 2013, : 2378 - 2383
  • [23] Steel Bars Counting and Splitting Method Based on Machine Vision
    Wu, Yang
    Zhou, Xiaofeng
    Zhang, Yichi
    2015 IEEE INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER), 2015, : 420 - 425
  • [24] Holonic based approach to machine vision
    Marin, F. B.
    Epureanu, A.
    Banu, M.
    Marinescu, V
    Constatin, I
    ACMOS '08: PROCEEDINGS OF THE 10TH WSEAS INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL, MODELLING AND SIMULATION, 2008, : 255 - +
  • [25] Deep Learning Based Traffic Accident Detection in Smart Transportation: A Machine Vision-Based Approach
    Melegrito, Mark
    Reyes, Ryan
    Tejada, Ryan
    Anthony, John Edgar Sualog
    Alon, Alvin Sarraga
    Delmo, Ritchelie P.
    Enaldo, Meriam A.
    Anqui, Abrahem P.
    2024 4TH INTERNATIONAL CONFERENCE ON APPLIED ARTIFICIAL INTELLIGENCE, ICAPAI, 2024, : 22 - 27
  • [26] Robust Lane Detection and Tracking Based on Machine Vision
    FAN Guotian
    LI Bo
    HAN Qin
    JIAO Rihua
    QU Gang
    ZTE Communications, 2020, 18 (04) : 69 - 77
  • [27] Path tracking of agricultural vehicle based on machine vision
    Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
    不详
    Nongye Jixie Xuebao, 2009, SUPPL. 1 (18-22):
  • [28] A novel algorithm of rebar counting on conveyor belt based on machine vision
    Nie, Zuoxian
    Hung, Mao-Hsiung
    Huang, Jing
    Journal of Information Hiding and Multimedia Signal Processing, 2016, 7 (02): : 425 - 437
  • [29] Research on Counting Algorithm of Residual Feeds in Aquaculture Based on Machine Vision
    Cao, Jiaheng
    Xu, Lihong
    2018 IEEE 3RD INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC), 2018, : 498 - 503
  • [30] Research on Cigarette Filter Rod Counting System Based on Machine Vision
    Qu, Hongjun
    Zhang, Peijian
    Zhang, Kun
    Wu, Jianguo
    ADVANCED COMPUTATIONAL METHODS IN LIFE SYSTEM MODELING AND SIMULATION, LSMS 2017, PT I, 2017, 761 : 513 - 523