1
|
Wu D, Han M, Song H, Song L, Duan Y. Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning. J Dairy Sci 2023; 106:2963-2979. [PMID: 36797189 DOI: 10.3168/jds.2022-22501] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 10/24/2022] [Indexed: 02/16/2023]
Abstract
Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observation of cow status because they physically occlude each other. In this study, we propose a method that can monitor the respiratory behavior of multiple cows. Initially, 4,000 manually labeled images were used to fine-tune the YOLACT (You Only Look At CoefficienTs) model for recognition and segmentation of multiple cows. Respiratory behavior in the resting state could better reflect their health status. Then, the specific resting states (lying resting, standing resting) of different cows were identified by fusing the convolutional neural network and bidirectional long and short-term memory algorithms. Finally, the corresponding detection algorithms (lying and standing resting) were used for respiratory behavior monitoring. The test results of 60 videos containing different interference factors indicated that the accuracy of respiratory behavior monitoring of multiple cows in 54 videos was >90.00%, and that of 4 videos was 100.00%. The average accuracy of the proposed method was 93.56%, and the mean absolute error and root mean square error were 3.42 and 3.74, respectively. Furthermore, the effectiveness of the method was analyzed for simultaneous monitoring of respiratory behavior of multiple cows under movement, occlusion disturbance, and behavioral changes. It was feasible to monitor the respiratory behavior of multiple cows based on the proposed algorithm. This study could provide an a priori technical basis for respiratory behavior monitoring and automatic diagnosis of respiratory-related diseases of multiple dairy cows based on biomedical engineering technology. In addition, it may stimulate researchers to develop robots with health-sensing functions that are oriented toward precision livestock farming.
Collapse
Affiliation(s)
- Dihua Wu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 712100; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 712100; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling, China 712100; School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China 310058
| | - Mengxuan Han
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 712100; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 712100; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling, China 712100
| | - Huaibo Song
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 712100; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 712100; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling, China 712100.
| | - Lei Song
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 712100; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 712100; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling, China 712100
| | - Yuanchao Duan
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 712100; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 712100; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling, China 712100
| |
Collapse
|
2
|
Zheng S, Zhou C, Jiang X, Huang J, Xu D. Progress on Infrared Imaging Technology in Animal Production: A Review. SENSORS 2022; 22:s22030705. [PMID: 35161450 PMCID: PMC8839879 DOI: 10.3390/s22030705] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 12/28/2021] [Accepted: 01/13/2022] [Indexed: 02/01/2023]
Abstract
Infrared thermography (IRT) imaging technology, as a convenient, efficient, and contactless temperature measurement technology, has been widely applied to animal production. In this review, we systematically summarized the principles and influencing parameters of IRT imaging technology. In addition, we also summed up recent advances of IRT imaging technology in monitoring the temperature of animal surfaces and core anatomical areas, diagnosing early disease and inflammation, monitoring animal stress levels, identifying estrus and ovulation, and diagnosing pregnancy and animal welfare. Finally, we made prospective forecast for future research directions, offering more theoretical references for related research in this field.
Collapse
Affiliation(s)
- Shuailong Zheng
- Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China; (S.Z.); (C.Z.)
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Changfan Zhou
- Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China; (S.Z.); (C.Z.)
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xunping Jiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jingshu Huang
- Agricultural Development Center of Hubei Province, Wuhan 430064, China;
| | - Dequan Xu
- Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China; (S.Z.); (C.Z.)
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
- Correspondence:
| |
Collapse
|
3
|
Borges Oliveira DA, Ribeiro Pereira LG, Bresolin T, Pontes Ferreira RE, Reboucas Dorea JR. A review of deep learning algorithms for computer vision systems in livestock. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104700] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|