Few-shot cow identification via meta-learning
Cow identification is a prerequisite for precision livestock farming. Biometric-based methods have made significant progress in cow identification. However, substantial labelling costs and frequent identification task changes are still hamper model application. In this work, a novel method called “M...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Information Processing in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317324000210 |
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| author | Xingshi Xu Yunfei Wang Yuying Shang Guangyuan Yang Zhixin Hua Zheng Wang Huaibo Song |
| author_facet | Xingshi Xu Yunfei Wang Yuying Shang Guangyuan Yang Zhixin Hua Zheng Wang Huaibo Song |
| author_sort | Xingshi Xu |
| collection | DOAJ |
| description | Cow identification is a prerequisite for precision livestock farming. Biometric-based methods have made significant progress in cow identification. However, substantial labelling costs and frequent identification task changes are still hamper model application. In this work, a novel method called “MFCI” was proposed to achieve accurate cow identification under few-shot and task-changing conditions. Specifically, the proposed method comprises two components: cow location and cow identification. First, an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images. Then, the Model-Agnostic Meta-Learning (MAML) framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows. Moreover, an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches. The experimental results showed that the proposed cow location model achieved a mAP of 99.5 %. The proposed cow identification model attained an accuracy of 90.43 % with only five samples per cow for 20 cows, outperforming other state-of-the-art methods. The results demonstrate the broad applicability and significant value of the proposed method. |
| format | Article |
| id | doaj-art-e98b78d37a254ad79d4bcda550e8433b |
| institution | DOAJ |
| issn | 2214-3173 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj-art-e98b78d37a254ad79d4bcda550e8433b2025-08-20T02:47:27ZengElsevierInformation Processing in Agriculture2214-31732025-03-01121809010.1016/j.inpa.2024.04.001Few-shot cow identification via meta-learningXingshi Xu0Yunfei Wang1Yuying Shang2Guangyuan Yang3Zhixin Hua4Zheng Wang5Huaibo Song6College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, ChinaCorresponding author.; College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, ChinaCow identification is a prerequisite for precision livestock farming. Biometric-based methods have made significant progress in cow identification. However, substantial labelling costs and frequent identification task changes are still hamper model application. In this work, a novel method called “MFCI” was proposed to achieve accurate cow identification under few-shot and task-changing conditions. Specifically, the proposed method comprises two components: cow location and cow identification. First, an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images. Then, the Model-Agnostic Meta-Learning (MAML) framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows. Moreover, an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches. The experimental results showed that the proposed cow location model achieved a mAP of 99.5 %. The proposed cow identification model attained an accuracy of 90.43 % with only five samples per cow for 20 cows, outperforming other state-of-the-art methods. The results demonstrate the broad applicability and significant value of the proposed method.http://www.sciencedirect.com/science/article/pii/S2214317324000210Precision livestockCattle identificationFew-shotTask-drivenAutoencoder |
| spellingShingle | Xingshi Xu Yunfei Wang Yuying Shang Guangyuan Yang Zhixin Hua Zheng Wang Huaibo Song Few-shot cow identification via meta-learning Information Processing in Agriculture Precision livestock Cattle identification Few-shot Task-driven Autoencoder |
| title | Few-shot cow identification via meta-learning |
| title_full | Few-shot cow identification via meta-learning |
| title_fullStr | Few-shot cow identification via meta-learning |
| title_full_unstemmed | Few-shot cow identification via meta-learning |
| title_short | Few-shot cow identification via meta-learning |
| title_sort | few shot cow identification via meta learning |
| topic | Precision livestock Cattle identification Few-shot Task-driven Autoencoder |
| url | http://www.sciencedirect.com/science/article/pii/S2214317324000210 |
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