Livestock Biometrics Identification Using Computer Vision Approaches: A Review
Abstract
:1. Introduction
2. Livestock Biometric Features
2.1. Retinal Vascular Patterns
2.2. Iris Patterns
2.3. Muzzle Pattern
2.4. Body Pattern
2.5. Ficial Features
2.6. Emerging Features
2.6.1. Three-Dimensional Visual Appearances and Skeleton Pose Features
2.6.2. Time-Series Features
3. Acquisition of Biometric Features of Livestock
3.1. Environment of Image Acquisition
3.2. Devices of Image Acquisition
4. Visual Biometric Identification for Livestock
4.1. Traditional Machine Learning Methods
4.2. Deep Learning Methods
4.2.1. One-Stage Model
4.2.2. Two-Stage Model
4.3. Hybrid Methods
5. Challenges and Future Directions in Visual Livestock Biometrics
5.1. Challenges in Livestock Biometrics Using Computer Vision
5.1.1. Challenges in Data Collection
- Demand for large-scale data
- Dataset imbalance
- Environmental interference
- Posture changes
5.1.2. Challenges Posed by Livestock Characteristics
- Data similarities
- Data dynamic changes
5.1.3. Challenges in Model Accuracy and Generalization
- Balance between model accuracy and complexity
- Improvement of model generalization
5.2. Research Hotspots and Trends in Visual Livestock Biometrics
5.2.1. Application of Feature Fusion and Multimodal Fusion Technology
5.2.2. Production of Large-Scale Benchmark Dataset
5.2.3. Multi Objects Tracking and Identification
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CNNs | Convolutional Neural Networks |
RNNs | Recurrent Neural Networks |
SURF | Speeded-Up Robust Features |
FAST | Features from Accelerated Segment Test |
DCNNs | Deep Convolutional Neural Networks |
SVM | Support Vector Machine |
LBP | Local Binary Pattern |
RGB-D | Red-Green-Blue–Depth |
LRCN | Long Short-Term Memory Convolutional Network |
SIFT | Scale-Invariant Feature Transform |
LDA | Linear Discriminant Analysis |
FLANN | Fast Library for Approximate Nearest Neighbors |
VGG | Visual Geometry Group |
Mask R-CNN | Mask Region-based Convolutional Neural Network |
GoogLeNet | Google Network |
MobileNet | Mobile Network |
LSTM | Long Short-Term Memory |
BiLSTM | Bidirectional Long Short-Term Memory |
YOLO | You Only Look Once |
Faster R-CNN | Faster Region-based Convolutional Neural Network |
SSD | Single Shot MultiBox Detector |
ResNet | Residual Network |
AlexNet | Alex Network |
SSL | Semi-Supervised Learning |
References
- Tonsor, G.T.; Schroeder, T.C. Livestock identification: Lessons for the US beef industry from the Australian system. J. Int. Food Agribus. Mark. 2006, 18, 103–118. [Google Scholar] [CrossRef]
- Ahmad, M.; Abbas, S.; Fatima, A.; Ghazal, T.M.; Alharbi, M.; Khan, M.A.; Elmitwally, N.S. AI-Driven livestock identification and insurance management system. Egypt. Inform. J. 2023, 24, 100390. [Google Scholar] [CrossRef]
- Bodkhe, J.; Dighe, H.; Gupta, A.; Bopche, L. Animal Identification. In Proceedings of the 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India, 28–29 December 2018; pp. 1–4. [Google Scholar]
- Zhao, J.; Li, A.; Jin, X.; Pan, L. Technologies in individual animal identification and meat products traceability. Biotechnol. Biotechnol. Equip. 2020, 34, 48–57. [Google Scholar] [CrossRef]
- Silveira, M. A Review of the History and Motivations of Animal Identification and the Different Methods of Animal Identification Focusing on Radiofrequency Identification and How It Works for the Development of a Radiofrequency Identification Based Herd Management System on the Cal Poly Dairy. 2013. Available online: https://www.researchgate.net/publication/303946440_A_Review_of_the_History_and_Motivations_of_Animal_Identification_and_the_Different_Methods_of_Animal_Identification_Focusing_on_Radiofrequency_Identification_and_How_It_Works_for_the_Development_of_a_ (accessed on 27 September 2023).
- Roberts, C.M. Radio frequency identification (RFID). Comput. Secur. 2006, 25, 18–26. [Google Scholar] [CrossRef]
- Duyck, J.; Finn, C.; Hutcheon, A.; Vera, P.; Salas, J.; Ravela, S. Sloop: A pattern retrieval engine for individual animal identification. Pattern Recognit. 2015, 48, 1059–1073. [Google Scholar] [CrossRef]
- Bugge, C.E.; Burkhardt, J.; Dugstad, K.S.; Enger, T.B.; Kasprzycka, M.; Kleinauskas, A.; Myhre, M.; Scheffler, K.; Ström, S.; Vetlesen, S. Biometric Methods of Animal Identification; Course notes; Laboratory Animal Science at the Norwegian School of Veterinary Science: Oslo, Norway, 2011; pp. 1–6. [Google Scholar]
- Chen, C.; Zhu, W.; Norton, T. Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning. Comput. Electron. Agric. 2021, 187, 106255. [Google Scholar] [CrossRef]
- Xu, P.; Zhang, Y.; Ji, M.; Guo, S.; Tang, Z.; Wang, X.; Guo, J.; Zhang, J.; Guan, Z. Advanced intelligent monitoring technologies for animals: A survey. Neurocomputing 2024, 585, 127640. [Google Scholar] [CrossRef]
- Zhou, L.X. Research on Sheep Face Recognition Method Based on Lightweight Neural Network. Master’s Thesis, Northwest A&F University, Yangling, China, 2022. [Google Scholar]
- Alsaadi, I.M. Physiological biometric authentication systems, advantages, disadvantages and future development: A review. Int. J. Sci. Technol. Res. 2015, 4, 285–289. [Google Scholar]
- Allen, A.; Golden, B.; Taylor, M.; Patterson, D.; Henriksen, D.; Skuce, R. Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland. Livest. Sci. 2008, 116, 42–52. [Google Scholar] [CrossRef]
- Barron, U.G.; Corkery, G.; Barry, B.; Butler, F.; McDonnell, K.; Ward, S. Assessment of retinal recognition technology as a biometric method for sheep identification. Comput. Electron. Agric. 2008, 60, 156–166. [Google Scholar] [CrossRef]
- Saygılı, A.; Cihan, P.; Ermutlu, C.Ş.; Aydın, U.; Aksoy, Ö. CattNIS: Novel identification system of cattle with retinal images based on feature matching method. Comput. Electron. Agric. 2024, 221, 108963. [Google Scholar] [CrossRef]
- Mustafi, S.; Ghosh, P.; Mandal, S.N. RetIS: Unique identification system of goats through retinal analysis. Comput. Electron. Agric. 2021, 185, 106127. [Google Scholar] [CrossRef]
- Cihan, P.; Saygili, A.; Ozmen, N.E.; Akyuzlu, M. Identification and Recognition of Animals from Biometric Markers Using Computer Vision Approaches: A Review. Kafkas Univ. Veter-Fak. Derg. 2023, 29, 581. [Google Scholar] [CrossRef]
- Alturk, G.; Karakus, F. Assessment of Retinal Recognition Technology as a Biometric Identification Method in Norduz Sheep. In Proceedings of the 11th International Animal Science Conference, Cappadocia, Turkey, 20–22 October 2019; pp. 20–22. [Google Scholar]
- Jain, A.K.; Nandakumar, K.; Ross, A. 50 years of biometric research: Accomplishments, challenges, and opportunities. Pattern Recognit. Lett. 2016, 79, 80–105. [Google Scholar] [CrossRef]
- Sheng, D.W. Research on Technology of Cattle’s Iris Recognition. Master’s Thesis, East China Normal University, Shanghai, China, 2010. [Google Scholar]
- Suzaki, M.; Yamakita, O.; Horikawa, S.i.; Kuno, Y.; Aida, H.; Sasaki, N.; Kusunose, R. A horse identification system using biometrics. Syst. Comput. Jpn. 2001, 32, 12–23. [Google Scholar] [CrossRef]
- He, X.; Yan, J.; Chen, G.; Shi, P. Contactless autofeedback iris capture design. IEEE Trans. Instrum. Meas. 2008, 57, 1369–1375. [Google Scholar]
- Lu, Y.; He, X.; Wen, Y.; Wang, P.S. A new cow identification system based on iris analysis and recognition. Int. J. Biom. 2014, 6, 18–32. [Google Scholar] [CrossRef]
- Trokielewicz, M.; Szadkowski, M. Iris and Periocular Recognition in Arabian Race Horses Using Deep Convolutional Neural Networks. In Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 1–4 October 2017; pp. 510–516. [Google Scholar]
- Larregui, J.I.; Cazzato, D.; Castro, S.M. An image processing pipeline to segment iris for unconstrained cow identification system. Open Comput. Sci. 2019, 9, 145–159. [Google Scholar] [CrossRef]
- Roy, S.; Dan, S.; Mukherjee, K.; Nath Mandal, S.; Hajra, D.K.; Banik, S.; Naskar, S. Black Bengal Goat Identification Using Iris Images. In Proceedings of the International Conference on Frontiers in Computing and Systems: COMSYS 2020, Jalpaiguri Government Engineering College (JGEC), West Bengal, India, 13–15 January 2020; pp. 213–224. [Google Scholar]
- Li, C.; Zhao, L.D. Research on Cattle Iris Localization Algorithm and Its Application in Meat Food Tracking and Traceability System. China Saf. Sci. J. 2011, 21, 124–130. [Google Scholar] [CrossRef]
- Sun, S.; Zhao, L. Bovine iris segmentation using region-based active contour model. Int. J. Innov. Comput. Inf. Control 2012, 8, 6461–6471. [Google Scholar]
- Laishram, M.; Mandal, S.N.; Haldar, A.; Das, S.; Bera, S.; Samanta, R. Biometric identification of Black Bengal goat: Unique iris pattern matching system vs deep learning approach. Anim. Biosci. 2023, 36, 980. [Google Scholar] [CrossRef] [PubMed]
- Yoon, H.; Park, M.; Lee, H.; An, J.; Lee, T.; Lee, S.-H. Deep learning framework for bovine iris segmentation. J. Anim. Sci. Technol. 2024, 66, 167. [Google Scholar] [CrossRef] [PubMed]
- Mishra, S.; Tomer, O.; Kalm, E. Muzzle dermatoglypics: A new method to identify bovines. Asian Livest. (FAO) 1995, 20, 91–96. [Google Scholar]
- Kumar, S.; Singh, S.K.; Singh, A.K. Muzzle point pattern based techniques for individual cattle identification. IET Image Process. 2017, 11, 805–814. [Google Scholar] [CrossRef]
- Noviyanto, A.; Arymurthy, A.M. Beef cattle identification based on muzzle pattern using a matching refinement technique in the SIFT method. Comput. Electron. Agric. 2013, 99, 77–84. [Google Scholar] [CrossRef]
- Barry, B.; Gonzales-Barron, U.; McDonnell, K.; Butler, F.; Ward, S. Using muzzle pattern recognition as a biometric approach for cattle identification. Trans. ASABE 2007, 50, 1073–1080. [Google Scholar] [CrossRef]
- Tharwat, A.; Gaber, T.; Hassanien, A.E. Cattle Identification Based on Muzzle Images Using Gabor Features and SVM Classifier. In Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Egypt, 28–30 November 2014; pp. 236–247. [Google Scholar]
- Taha, A.; Darwish, A.; Hassanien, A.E.; ElKholy, A. Arabian Horse Identification and Gender Determination System based on Feature Fusion and Gray Wolf Optimization. Int. J. Intell. Eng. Syst. 2020, 13, 145–155. [Google Scholar] [CrossRef]
- Li, G.; Erickson, G.E.; Xiong, Y. Individual beef cattle identification using muzzle images and deep learning techniques. Animals 2022, 12, 1453. [Google Scholar] [CrossRef]
- Zhao, K.X.; He, D.J. Recognition of individual dairy cattle based on convolutional neural networks. Trans. Chin. Soc. Agric. Eng. 2015, 31, 181–187. [Google Scholar]
- Zhao, K.; Jin, X.; Ji, J.; Wang, J.; Ma, H.; Zhu, X. Individual identification of Holstein dairy cows based on detecting and matching feature points in body images. Biosyst. Eng. 2019, 181, 128–139. [Google Scholar] [CrossRef]
- Zhang, R.; Ji, J.; Zhao, K.; Wang, J.; Zhang, M.; Wang, M. A cascaded individual cow identification method based on DeepOtsu and EfficientNet. Agriculture 2023, 13, 279. [Google Scholar] [CrossRef]
- He, S.; Schomaker, L. DeepOtsu: Document enhancement and binarization using iterative deep learning. Pattern Recognit. 2019, 91, 379–390. [Google Scholar] [CrossRef]
- Tan, M.; Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International conference on machine learning, Long Beach, CA, USA, 9–15 June 2019; pp. 6105–6114. [Google Scholar]
- Song, G.F. Research on Animal Facial Recognition Algorithm Based on Deep Learning. Master’s Thesis, Hangzhou Dianzi University, Hangzhou, China, 2019. [Google Scholar]
- Wiskott, L.; Fellous, J.-M.; Krüger, N.; Von Der Malsburg, C. Face recognition by elastic bunch graph matching. In Intelligent Biometric Techniques in Fingerprint and Face Recognition; Routledge: London, UK, 2022; pp. 355–396. [Google Scholar]
- Perronnin, F.; Sánchez, J.; Mensink, T. Improving the fisher kernel for large-scale image classification. In Proceedings of the Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, 5–11 September 2010; Part IV 11. pp. 143–156. [Google Scholar]
- Sihalath, T.; Basak, J.K.; Bhujel, A.; Arulmozhi, E.; Moon, B.E.; Kim, H.T. Pig identification using deep convolutional neural network based on different age range. J. Biosyst. Eng. 2021, 46, 182–195. [Google Scholar] [CrossRef]
- Liu, S.F.; Chang, R.; Li, B.; Wei, Y.; Wang, H.F.; Jia, N. Individual Identification of Cattle Based on RGB-D Images. Trans. Chin. Soc. Agric. Mach. 2023, 54, 260–266. [Google Scholar]
- Xuan, C.Z.; Lv, Y.; Liu, S.H.; Cui, J.H.; Zhang, X.W. Deep learning based identification of sheep face with fine-grained features. Digit. Agric. Intell. Agric. Mach. 2023, 26–30, 58. [Google Scholar] [CrossRef]
- Ahmad, M.; Abbas, S.; Fatima, A.; Issa, G.F.; Ghazal, T.M.; Khan, M.A. Deep transfer learning-based animal face identification model empowered with vision-based hybrid approach. Appl. Sci. 2023, 13, 1178. [Google Scholar] [CrossRef]
- Arslan, A.C.; Akar, M.; Alagöz, F. 3D cow identification in cattle farms. In Proceedings of the 2014 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey, 23–25 April 2014; pp. 1347–1350. [Google Scholar]
- Ferreira, R.E.; Bresolin, T.; Rosa, G.J.; Dórea, J.R. Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms. Comput. Electron. Agric. 2022, 201, 107272. [Google Scholar] [CrossRef]
- Zhang, F.Y. Individual Identity Recognition of Sheep Based on Deep Metric Learning. Master’s Thesis, Northwest A&F University, Yangling, China, 2023. [Google Scholar]
- SU, L.D. Study on Dairy Cows Gait Feature Extraction and Early Lameness Prediction. Ph.D. Thesis, Inner Mongolia Agricultural University, Hohhot, China, 2020. [Google Scholar]
- Qian, J.X. Gait Recognition of Pigs Based on Skeleton Analysis and Gait Energy Image. Master’s Thesis, Jiangsu University, Zhenjiang, China, 2018. [Google Scholar]
- Zhang, M.T.; Wang, M.M.; Liu, T.H.; Wen, S.D.; Yu, Y. Gait recognition in dairy cows based on skeleton energy maps. Jiangsu Agric. Sci. 2020, 48, 257–262. [Google Scholar] [CrossRef]
- Andrew, W.; Greatwood, C.; Burghardt, T. Fusing animal biometrics with autonomous robotics: Drone-based search and individual id of friesian cattle. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, Snowmass Village, CO, USA, 1–5 March 2020; pp. 38–43. [Google Scholar]
- Qiao, Y.; Clark, C.; Lomax, S.; Kong, H.; Su, D.; Sukkarieh, S. Automated individual cattle identification using video data: A unified deep learning architecture approach. Front. Anim. Sci. 2021, 2, 759147. [Google Scholar] [CrossRef]
- Hitelman, A.; Edan, Y.; Godo, A.; Berenstein, R.; Lepar, J.; Halachmi, I. Biometric identification of sheep via a machine-vision system. Comput. Electron. Agric. 2022, 194, 106713. [Google Scholar] [CrossRef]
- Mon, S.L.; Onizuka, T.; Tin, P.; Aikawa, M.; Kobayashi, I.; Zin, T.T. AI-enhanced real-time cattle identification system through tracking across various environments. Sci. Rep. 2024, 14, 17779. [Google Scholar] [CrossRef]
- Huang, Z.J.; Xu, A.J.; Zhou, S.Y.; Ye, J.H.; Weng, X.X.; Xiang, Y. Key point detection method for pig face fusing reparameterization and attention mechanisms. Trans. Chin. Soc. Agric. Eng. 2023, 39, 141–149. [Google Scholar]
- Song, S.; Liu, T.; Wang, H.; Hasi, B.; Yuan, C.; Gao, F.; Shi, H. Using pruning-based YOLOv3 deep learning algorithm for accurate detection of sheep face. Animals 2022, 12, 1465. [Google Scholar] [CrossRef] [PubMed]
- Andrew, W.; Greatwood, C.; Burghardt, T. Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy, 22–29 October 2017; pp. 2850–2859. [Google Scholar]
- Parmiggiani, A.; Liu, D.; Psota, E.; Fitzgerald, R.; Norton, T. Don’t get lost in the crowd: Graph convolutional network for online animal tracking in dense groups. Comput. Electron. Agric. 2023, 212, 108038. [Google Scholar] [CrossRef]
- Guo, Y.Y.; Hong, W.H.; Ding, Y.; Huang, X.P. Goat face detection method by combining coordinate attention mechanism and YOLO v5s model. Trans. Chin. Soc. Agric. Mach. 2023, 54, 313–321. [Google Scholar]
- Tassinari, P.; Bovo, M.; Benni, S.; Franzoni, S.; Poggi, M.; Mammi, L.M.E.; Mattoccia, S.; Di Stefano, L.; Bonora, F.; Barbaresi, A. A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Comput. Electron. Agric. 2021, 182, 106030. [Google Scholar] [CrossRef]
- Yao, C.; Li, Q.; Liu, G.; Lv, S.S.; Hou, C.; Zhang, M. Individual Identification of Partially Occluded Holstein Cows Based on NAS-Res. Trans. Chin. Soc. Agric. Mach. 2023, 54, 252–259. [Google Scholar]
- Pezzuolo, A.; Guarino, M.; Sartori, L.; Marinello, F. A feasibility study on the use of a structured light depth-camera for three-dimensional body measurements of dairy cows in free-stall barns. Sensors 2018, 18, 673. [Google Scholar] [CrossRef] [PubMed]
- Jaddoa, M.; Gonzalez, L.; Cuthbertson, H.; Al-Jumaily, A. Multi view face detection in cattle using infrared thermography. In Proceedings of the International Conference on Applied Computing to Support Industry: Innovation and Technology, Ramadi, Iraq, 15–16 September 2019; pp. 223–236. [Google Scholar]
- Kashiha, M.; Bahr, C.; Ott, S.; Moons, C.P.; Niewold, T.A.; Ödberg, F.O.; Berckmans, D. Automatic identification of marked pigs in a pen using image pattern recognition. Comput. Electron. Agric. 2013, 93, 111–120. [Google Scholar] [CrossRef]
- Viazzi, S.; Bahr, C.; Van Hertem, T.; Schlageter-Tello, A.; Romanini, C.; Halachmi, I.; Lokhorst, C.; Berckmans, D. Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows. Comput. Electron. Agric. 2014, 100, 139–147. [Google Scholar] [CrossRef]
- Wang, F.; Li, Q. Research on recognition method of individual cattle muzzle based on local invariant features. Heilongjiang Anim. Sci. Vet. Med. 2022, 2, 48–52+136–137. [Google Scholar] [CrossRef]
- Awad, A.I.; Hassaballah, M. Bag-of-visual-words for cattle identification from muzzle print images. Appl. Sci. 2019, 9, 4914. [Google Scholar] [CrossRef]
- Kumar, S.; Tiwari, S.; Singh, S.K. Face recognition of cattle: Can it be done? Proc. Natl. Acad. Sci. India Sect. A Phys. Sci. 2016, 86, 137–148. [Google Scholar] [CrossRef]
- Wang, M.M. Study on Individual Recognition of Cow Based on Gait Feature and Texture. Master’s Thesis, Hebei University of Technology, Tianjin, China, 2020. [Google Scholar]
- Andrew, W.; Hannuna, S.; Campbell, N.; Burghardt, T. Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery. In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25–28 September 2016; pp. 484–488. [Google Scholar]
- Huang, W.; Zhu, W.; Ma, C.; Guo, Y. Weber texture local descriptor for identification of group-housed pigs. Sensors 2020, 20, 4649. [Google Scholar] [CrossRef]
- Zhang, M.T.; Mi, N.; Yu, Y.; Shan, X.Y.; Yan, G.; Guo, Y.C. Individual identification of dairy cows based on feature fusion. Jiangsu Agric. Sci. 2018, 46, 278–281. [Google Scholar] [CrossRef]
- Zhao, L.; Zhou, G.H.; Ren, L.S. Individual identification of dairy cows based on comprehensive face and trunk information. J. Hebei Agric. Univ. 2024, 47, 112–118. [Google Scholar] [CrossRef]
- Li, Z.H.; Wang, T.Y.; Li, Y.Z. Recognition of sheep individual based on GoogLeNet combined with attention mechanism. Intell. Comput. Appl. 2023, 13, 148–153. [Google Scholar]
- Pang, Y.; Yu, W.; Zhang, Y.; Xuan, C.; Wu, P. Sheep face recognition and classification based on an improved MobilenetV2 neural network. Int. J. Adv. Robot. Syst. 2023, 20, 17298806231152969. [Google Scholar] [CrossRef]
- Andrew, W.; Gao, J.; Mullan, S.; Campbell, N.; Dowsey, A.W.; Burghardt, T. Visual identification of individual Holstein-Friesian cattle via deep metric learning. Comput. Electron. Agric. 2021, 185, 106133. [Google Scholar] [CrossRef]
- Shen, W.; Hu, H.; Dai, B.; Wei, X.; Sun, J.; Jiang, L.; Sun, Y. Individual identification of dairy cows based on convolutional neural networks. Multimed. Tools Appl. 2020, 79, 14711–14724. [Google Scholar] [CrossRef]
- Xue, H.; Qin, J.; Quan, C.; Ren, W.; Gao, T.; Zhao, J. Open set sheep face recognition based on Euclidean space metric. Math. Probl. Eng. 2021, 2021, 3375394. [Google Scholar] [CrossRef]
- Xing, Y.; Wu, B.; Wu, S. Individual cow recognition based on convolution neural network and transfer learning. Laser Optoelectron. Prog. 2021, 58, 1628002. [Google Scholar]
- Hou, H.; Shi, W.; Guo, J.; Zhang, Z.; Shen, W.; Kou, S. Cow rump identification based on lightweight convolutional neural networks. Information 2021, 12, 361. [Google Scholar] [CrossRef]
- Shojaeipour, A.; Falzon, G.; Kwan, P.; Hadavi, N.; Cowley, F.C.; Paul, D. Automated muzzle detection and biometric identification via few-shot deep transfer learning of mixed breed cattle. Agronomy 2021, 11, 2365. [Google Scholar] [CrossRef]
- Zhang, C.; Zhang, H.; Tian, F.; Zhou, Y.; Zhao, S.; Du, X. Research on sheep face recognition algorithm based on improved AlexNet model. Neural Comput. Appl. 2023, 35, 24971–24979. [Google Scholar] [CrossRef]
- Hu, H.; Dai, B.; Shen, W.; Wei, X.; Sun, J.; Li, R.; Zhang, Y. Cow identification based on fusion of deep parts features. Biosyst. Eng. 2020, 192, 245–256. [Google Scholar] [CrossRef]
- Marsot, M.; Mei, J.; Shan, X.; Ye, L.; Feng, P.; Yan, X.; Li, C.; Zhao, Y. An adaptive pig face recognition approach using Convolutional Neural Networks. Comput. Electron. Agric. 2020, 173, 105386. [Google Scholar] [CrossRef]
- Xiao, J.; Liu, G.; Wang, K.; Si, Y. Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM. Comput. Electron. Agric. 2022, 194, 106738. [Google Scholar] [CrossRef]
- Du, Y.; Kou, Y.; Li, B.; Qin, L.; Gao, D. Individual identification of dairy cows based on deep learning and feature fusion. Anim. Sci. J. 2022, 93, e13789. [Google Scholar] [CrossRef] [PubMed]
- Tang, Z.Y. Feeding Behavior and Identification of Pigs Based on Improved Optical Flow Method and Deep Learning. Master’s Thesis, JiangSu University, Zhenjiang, China, 2022. [Google Scholar]
- Achour, B.; Belkadi, M.; Filali, I.; Laghrouche, M.; Lahdir, M. Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on Convolutional Neural Networks (CNN). Biosyst. Eng. 2020, 198, 31–49. [Google Scholar] [CrossRef]
- Qiao, Y.; Kong, H.; Clark, C.; Lomax, S.; Su, D.; Eiffert, S.; Sukkarieh, S. Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation. Comput. Electron. Agric. 2021, 185, 106143. [Google Scholar] [CrossRef]
- Zhang, X.; Xuan, C.; Ma, Y.; Tang, Z.; Gao, X. An efficient method for multi-view sheep face recognition. Eng. Appl. Artif. Intell. 2024, 134, 108697. [Google Scholar] [CrossRef]
- Salama, A.; Hassanien, A.E.; Fahmy, A. Sheep identification using a hybrid deep learning and bayesian optimization approach. IEEE Access 2019, 7, 31681–31687. [Google Scholar] [CrossRef]
- Ferreira, R.E.; Lee, Y.J.; Dórea, J.R. Using pseudo-labeling to improve performance of deep neural networks for animal identification. Sci. Rep. 2023, 13, 13875. [Google Scholar] [CrossRef] [PubMed]
- Shang, C. Research on Individual Identification of Goat Based on Deep Learning. Master’s Thesis, NorthWest A&F University, Yangling, China, 2022. [Google Scholar]
- Liu, H. Cattle Identification in Complex Scenes. Master’s Thesis, HangZhou Dianzi University, Hangzhou, China, 2023. [Google Scholar]
- Wang, B.; Li, X.; An, X.; Duan, W.; Wang, Y.; Wang, D.; Qi, J. Open-Set Recognition of Individual Cows Based on Spatial Feature Transformation and Metric Learning. Animals 2024, 14, 1175. [Google Scholar] [CrossRef] [PubMed]
- Li, G.; Jiao, J.; Shi, G.; Ma, H.; Gu, L.; Tao, L. Fast Recognition of Pig Faces Based on Improved Yolov3. In Proceedings of the International Conference on Computer, Big Data and Artificial Intelligence (ICCBDAI 2021); Journal of Physics: Conference Series, Beihai, China, 12–14 December 2021; IOP Publishing Ltd.: Bristol, UK, 2022; p. 12005. [Google Scholar]
- Hu, Z.; Yang, H.; Lou, T.T. Instance detection of group breeding pigs using a pyramid network with dual attention feature. Trans. Chin. Soc. Agric. Eng. 2021, 37, 166–174. [Google Scholar]
- Li, S.; Kang, X.; Feng, Y.; Liu, G. Detection Method for Individual Pig Based on Improved YOLOv4 Convolutional Neural Network. In Proceedings of the 2021 4th International Conference on Data Science and Information Technology, Shanghai, China, 23–25 July 2021; pp. 231–235. [Google Scholar]
- Yang, S.Q.; Liu, Y.Q.H.; Wang, Z.; Han, Y.Y.; Wang, Y.S.; Lan, X.Y. Improved YOLO V4 model for face recognition of diary cow by fusing coordinate information. Trans. Chin. Soc. Agric. Eng. 2021, 37, 129–135. [Google Scholar]
- Xu, X.S.; Wang, Y.F.; Deng, H.X.; Song, H.B. Nighttime cattle face recognition based on cross-modal shared feature learning. J. South China Agric. Univ. 2024, 45, 793–801. [Google Scholar]
- Xue, J.; Hou, Z.; Xuan, C.; Ma, Y.; Sun, Q.; Zhang, X.; Zhong, L. A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching. Animals 2024, 14, 1923. [Google Scholar] [CrossRef]
- Weng, Z.; Meng, F.; Liu, S.; Zhang, Y.; Zheng, Z.; Gong, C. Cattle face recognition based on a Two-Branch convolutional neural network. Comput. Electron. Agric. 2022, 196, 106871. [Google Scholar] [CrossRef]
- Zhang, X.; Xuan, C.; Ma, Y.; Su, H.; Zhang, M. Biometric facial identification using attention module optimized YOLOv4 for sheep. Comput. Electron. Agric. 2022, 203, 107452. [Google Scholar] [CrossRef]
- Wan, Z.; Tian, F.; Zhang, C. Sheep face recognition model based on deep learning and bilinear feature fusion. Animals 2023, 13, 1957. [Google Scholar] [CrossRef] [PubMed]
- Lv, Y. Research on Sheep Face Identity Recognition Based on Improved Deep Convolutional Neural Network. Master’s Thesis, Inner Mongolia Agricultural University, Hohhot, China, 2023. [Google Scholar]
- Wang, Y.; Xu, X.; Wang, Z.; Li, R.; Hua, Z.; Song, H. ShuffleNet-Triplet: A lightweight RE-identification network for dairy cows in natural scenes. Comput. Electron. Agric. 2023, 205, 107632. [Google Scholar] [CrossRef]
- Zhang, X.; Xuan, C.; Ma, Y.; Tang, Z.; Cui, J.; Zhang, H. High-similarity sheep face recognition method based on a Siamese network with fewer training samples. Comput. Electron. Agric. 2024, 225, 109295. [Google Scholar] [CrossRef]
- Chen, X.; Yang, T.; Mai, K.; Liu, C.; Xiong, J.; Kuang, Y.; Gao, Y. Holstein cattle face re-identification unifying global and part feature deep network with attention mechanism. Animals 2022, 12, 1047. [Google Scholar] [CrossRef]
- Zhang, J.L.; Zhou, K.; Zhuang, Y.R.; Teng, G.H. Effect of facial changes on the accuracy of the recognition model during the growth of finishing pigs. J. China Agric. Univ. 2021, 26, 180–186. [Google Scholar]
- Fu, L.L.; Li, S.J.; Kong, S.L.; Gong, H.; Li, S.H. Research on individual identification of cows based on Multi-Light model. Heilongjiang Anim. Sci. Vet. Med. 2023, 41–45+51+132–133. [Google Scholar] [CrossRef]
- Li, X.; Xiang, Y.; Li, S. Combining convolutional and vision transformer structures for sheep face recognition. Comput. Electron. Agric. 2023, 205, 107651. [Google Scholar] [CrossRef]
- Wang, Z.; Liu, T. Two-stage method based on triplet margin loss for pig face recognition. Comput. Electron. Agric. 2022, 194, 106737. [Google Scholar] [CrossRef]
- Li, X.; Du, J.; Yang, J.; Li, S. When mobilenetv2 meets transformer: A balanced sheep face recognition model. Agriculture 2022, 12, 1126. [Google Scholar] [CrossRef]
- Li, X.; Zhang, Y.; Li, S. SheepFaceNet: A Speed–Accuracy Balanced Model for Sheep Face Recognition. Animals 2023, 13, 1930. [Google Scholar] [CrossRef] [PubMed]
- Ma, C.; Sun, X.; Yao, C.; Tian, M.; Li, L. Research on sheep recognition algorithm based on deep learning in animal husbandry. J. Phys. Conf. Ser. 2020, 1651, 12129. [Google Scholar] [CrossRef]
- Zhang, X.; Xuan, C.; Xue, J.; Chen, B.; Ma, Y. LSR-YOLO: A high-precision, lightweight model for sheep face recognition on the mobile end. Animals 2023, 13, 1824. [Google Scholar] [CrossRef] [PubMed]
- Bati, C.T.; Ser, G. Improved sheep identification and tracking algorithm based on YOLOv5+ SORT methods. Signal Image Video Process. 2024, 18, 1–12. [Google Scholar] [CrossRef]
- Wang, R.; Gao, R.; Li, Q.; Dong, J. Pig face recognition based on metric learning by combining a residual network and attention mechanism. Agriculture 2023, 13, 144. [Google Scholar] [CrossRef]
- Bakhshayeshi, I.; Erfani, E.; Taghikhah, F.R.; Elbourn, S.; Beheshti, A.; Asadnia, M. An Intelligence Cattle Reidentification System Over Transport by Siamese Neural Networks and YOLO. IEEE Internet Things J. 2023, 11, 2351–2363. [Google Scholar] [CrossRef]
- Okura, F.; Ikuma, S.; Makihara, Y.; Muramatsu, D.; Nakada, K.; Yagi, Y. RGB-D video-based individual identification of dairy cows using gait and texture analyses. Comput. Electron. Agric. 2019, 165, 104944. [Google Scholar] [CrossRef]
- Li, D.; Li, B.; Li, Q.; Wang, Y.; Yang, M.; Han, M. Cattle identification based on multiple feature decision layer fusion. Sci. Rep. 2024, 14, 26631. [Google Scholar] [CrossRef]
- Bo, L.; Yuefeng, L.; Xiang, B.; Yue, W.; Haofeng, L.; Xuan, L. Research on dairy cow identification methods in dairy farm. Indian J. Anim. Res. 2023, 57, 1733–1739. [Google Scholar] [CrossRef]
- De La Torre, M.P.; Briefer, E.F.; Ochocki, B.M.; McElligott, A.G.; Reader, T. Mother–offspring recognition via contact calls in cattle, Bos taurus. Anim. Behav. 2016, 114, 147–154. [Google Scholar] [CrossRef]
- Briefer, E.F.; Sypherd, C.C.-R.; Linhart, P.; Leliveld, L.M.; Padilla de La Torre, M.; Read, E.R.; Guérin, C.; Deiss, V.; Monestier, C.; Rasmussen, J.H. Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production. Sci. Rep. 2022, 12, 3409. [Google Scholar] [CrossRef] [PubMed]
- Pang, Y.; Yu, W.; Xuan, C.; Zhang, Y.; Wu, P. A Large Benchmark Dataset for Individual Sheep Face Recognition. Agriculture 2023, 13, 1718. [Google Scholar] [CrossRef]
- Guo, Q.; Sun, Y.; Orsini, C.; Bolhuis, J.E.; de Vlieg, J.; Bijma, P.; de With, P.H. Enhanced camera-based individual pig detection and tracking for smart pig farms. Comput. Electron. Agric. 2023, 211, 108009. [Google Scholar] [CrossRef]
- Guan, H.; Motohashi, N.; Maki, T.; Yamaai, T. Cattle identification and activity recognition by surveillance camera. Electron. Imaging 2020, 32, 1–6. [Google Scholar] [CrossRef]
Scenarios | Objects | Setup of Environment | Features | |
---|---|---|---|---|
locative scenarios | Assaf breed sheep; Acquisition of facial images | 1. NVIDIA Jetson Nano embedded system-on-module (SoM), 2. front camera for recording facial videos, 3. side camera for recording ear tags, and 4. Infrared (IR) sensor [58]. | 1. The imaging device is affixed to a regulated water trough. Video data are acquired in a contactless manner while the sheep are engaged in the act of drinking independently. 2. The two cameras were positioned at a distance of 80 cm from the reference point. | 1. The image data are less susceptible to external factors (such as animal movement, obstructions) and exhibits high clarity. 2. A stable acquisition environment serves to reduce errors and facilitate accurate data analysis. 3. The costs associated with these methods are higher, whether the shooting is man-made (which is time-consuming) or the livestock is fixed (which requires more sophisticated equipment). |
Dairy cows; Acquisition of back images | 1. Record the cattle walking through the exit lane of the milking parlor. 2. The camera is located above the 4 m off the ground [59]. | |||
Dan line sow; Acquisition of face images | Data were collected in the mating barn, and the images captured in the sow restriction pen featured only a single pig [60]. | |||
Sunit Sheep; Acquisition of facial images | The sheep were taken in turn to an enclosed environment and photographed individually by an experimenter holding a camera while they were calm [61]. | |||
open scenarios | Holstein cows; Acquisition of back images | 1. Outdoor farmland. 2. Photographed by a drone (DJI Inspire MkI) at 5 m above the ground [62]. | 1. Livestock are relatively free to move, and data can be obtained when they are in a natural state. 2. Relatively low cost. 3. The environment is complex and variable, and the acquired image data may be blurred or have a lot of noise. 4. High requirements for image processing techniques and methods. | |
Pigs; Acquisition of dorsal images | The camera is set orthogonal to the plane of the pen and mounted on the ceiling [63]. | |||
Huanghuai goat; Acquisition of facial images | Images of goats in their natural state were captured and acquired by mobile phone in an actual rearing environment [64]. |
Type | Device | Setup | Acquisition Image | Reference |
---|---|---|---|---|
2D | Panasonic WV—BP330 Video Format: MPEG-1 Frame rate: 25 fps Resolution: 576 × 720 Data Rate: 64 kbps | Cameras were installed in the rafters to capture top-view images; To provide light in the barn, six 58 W, 120 cm Gamma white fluorescent tube lamps were installed at a height of 200 cm in locations | Image of a small group of pigs in a barn | Kashiha et al. (2013) [69] |
Nikon D5200 Resolution: 720 × 1280 | The camera was installed on a tripod 3.5 m away from path and 1.5 m above the ground | Individual image of cattle | Zhao et al. (2019) [39] | |
IriShield–USB MK2120U Resolution: 640 × 480 | Camera was connected with a light weighted mobile device through the cable for capturing iris images within a distance of 5 cm from the sensor. Eye lid and eye lashes are avoided as much as possible to visualize the whole portion of the iris during capture. | Localized image: Iris images of goat | Laishram et al. (2023) [29] | |
3D | Kinect XBOX 360 Resolution: (RGB):640 × 480 (Depth): 320 × 240 | The cameras were placed at a 45 degrees angle, 3.28 m apart from each other and 2.7 m above the ground. | Individual image of cattle | Arslan et al. (2014) [50] |
Kinect V2 Resolution: (RGB): 1920 × 1080 (Depth): 512 × 424 | All videos were recorded using Kinect for Windows SDK 2.0 installed on a laptop locally operated by a person who manually started recording as soon as the calf was positioned on the scale, and stopped recording when the weighing process was concluded for that calf. | Individual image of cattle | Ferreira et al. (2022) [51] | |
Intel RealSenseD455 Resolution (Depth): 640 × 480 | The camera was installed on a tripod 1.5 m away from the barn fence in order to capture and store RGB-D images of the cow’s face using the camera’s own software (Intel. RealSense. Viewer). | Localized image: face of cattle | Liu et al. (2024) [47] | |
Thermal infrared imaging | AGEMA 590 PAL, Therma Cam S65, A310, T335 Resolution: 320 × 240 | The camera was setup at approximately 2 m from the target cattle as they moved through the race towards the knocking box. All cattle were in the shade under the roof during the recordings. And the camera was setup at approximately 45 degrees angle from the head of the animal. | Individual image of cattle | Jaddoa et al. (2019) [68] |
Features | Livestock/Images (Train, Test) | Recognition Methods | Accuracy | Reference |
---|---|---|---|---|
Overall cow object | 93/(593, 365) | YOLO + three independent CNNs + SVM | 98.36% | Hu et al. (2020) [88] |
Pig face | 10/(2044, 320) | Haar cascade classifiers + CNN model | 83% | Marsot et al. (2020) [89] |
Cattle body | 147(farm A)/- 13(farm B)/- 1103(farm C)/- | YOLOV8 + VGG16 + SVM | 96.34% (three farms average) | Mon et al. (2024) [59] |
Cow’ back | 48/- | Mask R-CNN + SVM | 98.67% | Xiao et al. (2022) [90] |
Horse face | -/(1000, 103) | YOLOV7 + SIFT + FLANN | 99.5% | Ahmad et al. (2023) [49] |
Dairy cow’ trunk | 34/(480, 206) | VGG16 + SVM | 99.48% | Du et al. (2022) [91] |
Pig’ back | \ | MB-ACDLDP + PIG-VGG16 | 94.52% | Tang (2022) [92] |
Dairy cow’ head | 17/- | CNN + SVM | 96.72% | Achour et al. (2020) [93] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Meng, H.; Zhang, L.; Yang, F.; Hai, L.; Wei, Y.; Zhu, L.; Zhang, J. Livestock Biometrics Identification Using Computer Vision Approaches: A Review. Agriculture 2025, 15, 102. https://doi.org/10.3390/agriculture15010102
Meng H, Zhang L, Yang F, Hai L, Wei Y, Zhu L, Zhang J. Livestock Biometrics Identification Using Computer Vision Approaches: A Review. Agriculture. 2025; 15(1):102. https://doi.org/10.3390/agriculture15010102
Chicago/Turabian StyleMeng, Hua, Lina Zhang, Fan Yang, Lan Hai, Yuxing Wei, Lin Zhu, and Jue Zhang. 2025. "Livestock Biometrics Identification Using Computer Vision Approaches: A Review" Agriculture 15, no. 1: 102. https://doi.org/10.3390/agriculture15010102
APA StyleMeng, H., Zhang, L., Yang, F., Hai, L., Wei, Y., Zhu, L., & Zhang, J. (2025). Livestock Biometrics Identification Using Computer Vision Approaches: A Review. Agriculture, 15(1), 102. https://doi.org/10.3390/agriculture15010102