Progress of computer vision and deep learning methods for pig’s identity and behavior recognition

doi:10.13300/j.cnki.hnlkxb.2023.03.006

Home > Archive>Volume 42, Issue 3, 2023 >47-56. DOI:10.13300/j.cnki.hnlkxb.2023.03.006

Progress of computer vision and deep learning methods for pig’s identity and behavior recognition
DOI:
                        10.13300/j.cnki.hnlkxb.2023.03.006
                    
CSTR:
                        [cstr]
                    
Author:
                        LIU Feng1,2,3LIU Feng
College of Informatics， Huazhong Agricultural University/Key Laboratory of Intelligent Technology in Animal Husbandry， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Smart Agricultural Technology， Ministry of Education/ Hubei Province Research Center of Engineering Technology of Agricultural Big Data， Wuhan 430070，China;Shenzhen Institute of Nutrition and Health， Huazhong Agricultural University， Shenzhen 518000，China;Agricultural Genomics Institute at Shenzhen， Chinese Academy of Agricultural Sciences/ Shenzhen Branch， Guangdong Laboratory for Lingnan Modern Agriculture， Shenzhen 518000，China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
WU Wenjie1WU Wenjie
College of Informatics， Huazhong Agricultural University/Key Laboratory of Intelligent Technology in Animal Husbandry， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Smart Agricultural Technology， Ministry of Education/ Hubei Province Research Center of Engineering Technology of Agricultural Big Data， Wuhan 430070，China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
LIU Xiaolei2,3,4LIU Xiaolei
Shenzhen Institute of Nutrition and Health， Huazhong Agricultural University， Shenzhen 518000，China;Agricultural Genomics Institute at Shenzhen， Chinese Academy of Agricultural Sciences/ Shenzhen Branch， Guangdong Laboratory for Lingnan Modern Agriculture， Shenzhen 518000，China;Key Laboratory of Swine Genetics and Breeding， Ministry of Agriculture， Wuhan 430070，China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
WANG Xinran1WANG Xinran
College of Informatics， Huazhong Agricultural University/Key Laboratory of Intelligent Technology in Animal Husbandry， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Smart Agricultural Technology， Ministry of Education/ Hubei Province Research Center of Engineering Technology of Agricultural Big Data， Wuhan 430070，China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
FANG Yaping1FANG Yaping
College of Informatics， Huazhong Agricultural University/Key Laboratory of Intelligent Technology in Animal Husbandry， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Smart Agricultural Technology， Ministry of Education/ Hubei Province Research Center of Engineering Technology of Agricultural Big Data， Wuhan 430070，China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
LI Guoliang1,2,3LI Guoliang
College of Informatics， Huazhong Agricultural University/Key Laboratory of Intelligent Technology in Animal Husbandry， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Smart Agricultural Technology， Ministry of Education/ Hubei Province Research Center of Engineering Technology of Agricultural Big Data， Wuhan 430070，China;Shenzhen Institute of Nutrition and Health， Huazhong Agricultural University， Shenzhen 518000，China;Agricultural Genomics Institute at Shenzhen， Chinese Academy of Agricultural Sciences/ Shenzhen Branch， Guangdong Laboratory for Lingnan Modern Agriculture， Shenzhen 518000，China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
DU Xiaoyong1,2,3,4DU Xiaoyong
College of Informatics， Huazhong Agricultural University/Key Laboratory of Intelligent Technology in Animal Husbandry， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Smart Agricultural Technology， Ministry of Education/ Hubei Province Research Center of Engineering Technology of Agricultural Big Data， Wuhan 430070，China;Shenzhen Institute of Nutrition and Health， Huazhong Agricultural University， Shenzhen 518000，China;Agricultural Genomics Institute at Shenzhen， Chinese Academy of Agricultural Sciences/ Shenzhen Branch， Guangdong Laboratory for Lingnan Modern Agriculture， Shenzhen 518000，China;Key Laboratory of Swine Genetics and Breeding， Ministry of Agriculture， Wuhan 430070，China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site

                    
Affiliation:1.College of Informatics， Huazhong Agricultural University/Key Laboratory of Intelligent Technology in Animal Husbandry， Ministry of Agriculture and Rural Affairs/ Engineering Research Center of Smart Agricultural Technology， Ministry of Education/ Hubei Province Research Center of Engineering Technology of Agricultural Big Data， Wuhan 430070，China;2.Shenzhen Institute of Nutrition and Health， Huazhong Agricultural University， Shenzhen 518000，China;3.Agricultural Genomics Institute at Shenzhen， Chinese Academy of Agricultural Sciences/ Shenzhen Branch， Guangdong Laboratory for Lingnan Modern Agriculture， Shenzhen 518000，China;4.Key Laboratory of Swine Genetics and Breeding， Ministry of Agriculture， Wuhan 430070，China
Clc Number:S817.3
Fund Project:

Article

Figures

Metrics

Reference

Cited by

Materials

Comments

Abstract:

It is an important study direction in the area of smart farming to explore the combination of new progress in the field of pig farming with artificial intelligence. Among them， how to automatically identify the individual identity and behavior of pigs is a key and hot issue to be solved in the current pig breeding industry. This article summarizes the existed methods of using deep neural networks to identify the individual identity and behavior of pigs based on the progress of computer vision and deep learning models in human recognition. The problems in the existed methods are analyzed， and the key study directions in the future are proposed. Five aspects urgently needed to be developed are as follows：（1） the methods of accurately extracting the features of pig’s identity and behavior under the conditions that pig’s behavior cannot be controlled and the key parts of pig’s body are often contaminated；（2） the deep learning models based on computer vision that dedicate for pigs to recognize the identity and behavior based on the specific features of pigs；（3） the studies on multi-task deep learning models that can recognize pig’s identity and behavior simultaneously；（4）the studies on general-purpose pig behavior recognition methods based on basic postures and movements that are applicable to multiple scenarios；（5） the studies on the deployment methods of pig identification and behavior recognition based on edge computing.

Key words:identity recognition;behavior recognition;deep learning;computer vision;smart farming;pig face recognition;automatic monitoring

Get Citation

刘峰,吴文杰,刘小磊,王欣然,方亚平,李国亮,杜小勇. Progress of computer vision and deep learning methods for pig’s identity and behavior recognition[J]. Jorunal of Huazhong Agricultural University,2023,42(3):47-56.

Copy

Article Metrics

Abstract:
PDF:
HTML:
Cited by:

History

Received:October 31,2022
Revised:
Adopted:
Online: June 20,2023
Published:

Get Citation

Share

Article Metrics

History

Article QR Code

Contact Us

Link