1.华中农业大学信息学院/农业农村部智慧养殖技术重点实验室/ 农业智能技术教育部工程研究中心/湖北省农业大数据工程技术研究中心，武汉 430070;2.华中农业大学深圳营养与健康研究院，深圳 518000;3.中国农业科学院深圳农业基因组研究所/岭南现代农业科学与技术广东省实验室深圳分中心，深圳 518000;4.农业动物遗传育种与繁殖教育部重点实验室，武汉 430070
刘峰，E-mail： liufeng@mail.hzau.edu.cn
杜小勇，E-mail：duxiaoyong@mail.hzau.edu.cn
S817.3
华中农业大学-中国农业科学院深圳农业基因组研究所合作基金（SZYJY2021011；SZYJY2022001）；国家重点研发计划青年科学家项目（2021YFD1300800）
- LIU Feng 1,2,3
LIU 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
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在本站中查找 - WU Wenjie 1
WU 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
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在本站中查找 - LIU Xiaolei 2,3,4
LIU 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
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在本站中查找 - WANG Xinran 1
WANG 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
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在本站中查找 - FANG Yaping 1
FANG 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
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在本站中查找 - LI Guoliang 1,2,3
LI 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
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在本站中查找 - DU Xiaoyong 1,2,3,4
DU 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
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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
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探索人工智能领域新技术与生猪养殖相结合，是当前智慧养殖领域的一个重要研究方向。其中，如何自动地识别猪只个体身份与行为，是当前生猪养殖行业要解决的一个关键问题。为推动计算机视觉和深度学习技术在猪只健康状态智能化监测方面的应用，本文先分析了基于计算机视觉与深度神经网络的人的身份及行为识别模型的研究进展，然后对利用计算机视觉与深度神经网络识别猪只个体身份及行为的方法进行了归纳总结，并指出已有方法中存在的问题，最后提出了下一步的重点研究方向：（1）在猪只运动不可控及关键特征部位受到污染的情况下，准确提取其身份及行为特征的方法研究；（2）针对猪只身份及行为特征的基于计算机视觉的原创性深度学习模型的研究；（3）能够同时检测猪只身份及行为的多任务神经网络的研究；（4）适用于多场景的基于基础姿态及动作的通用型猪只行为识别方法的研究；（5）基于边缘计算的猪只个体身份及行为识别的部署方法研究。
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.
引用本文
刘峰,吴文杰,刘小磊,王欣然,方亚平,李国亮,杜小勇.计算机视觉与深度学习在猪只识别中的研究进展[J].华中农业大学学报,2023,42(3):47-56
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- 收稿日期:2022-10-31
- 在线发布日期: 2023-06-20
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