Paper Info
Title
Intelligent Grazing UAV Based on Airborne Depth Reasoning
Abstract
The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of "collectivization" and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.
Year
DOI
Venue
2022
10.3390/rs14174188
REMOTE SENSING
Keywords
DocType
Volume
precision grazing, intelligent UAV, cattle monitoring, YOLOv5, Inception V3, LSTM
Journal
14
Issue
ISSN
Citations 
17
2072-4292
0
PageRank 
References 
Authors
0.34
0
15
Name
Order
Citations
PageRank
Wei Luo101.35
Ze Zhang200.34
Ping Fu311.42
Guosheng Wei400.34
Dongliang Wang501.01
Xuqing Li600.34
Quanqin Shao7136.11
Yuejun He800.34
Huijuan Wang900.34
Zihui Zhao1000.34
Ke Liu1100.34
Yuyan Liu1200.34
Yongxiang Zhao1300.34
Suhua Zou1400.34
Xueli Liu1500.34