ArticleSensors (Basel, Switzerland)2022
Livestock Identification Using Deep Learning for Traceability.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 29 citations in OpenAlex.
- Article
- Article
- Computer Vision in Dairy Farm Management: A Literature Review of Current Applications and Future Perspectives.Animals : an open access journal from MDPI · 2025Review
- Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring-A Critical Review.Animals : an open access journal from MDPI · 2025Review
- Dynamic Multi-Behaviour, Orientation-Invariant Re-Identification of Holstein-Friesian Cattle.Sensors (Basel, Switzerland) · 2025Article
- AI and Data Analytics in the Dairy Farms: A Scoping Review.Animals : an open access journal from MDPI · 2025Review
- CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement.Sensors (Basel, Switzerland) · 2025Article
- Research trends in livestock facial identification: a review.Journal of animal science and technology · 2025Review
- SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition.Sensors (Basel, Switzerland) · 2024Article
- Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.