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
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