ReviewJournal of animal science and technology2025
Research trends in livestock facial identification: a review.
Review in Journal of animal science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification.Journal of imaging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review examines the application of video processing and convolutional neural network (CNN)-based deep learning for animal face recognition, identification, and re-identification. These technologies are essential for precision livestock farming, addressing challenges in production efficiency, animal welfare, and environmental impact. With advancements in computer technology, livestock monitoring systems have evolved into sensor-based contact methods and video-based non-contact methods. Recent developments in deep learning enable the continuous analysis of accumulated data, automating the monitoring of animal conditions. By integrating video processing with CNN-based deep learning, it is possible to estimate growth, identify individuals, and monitor behavior more effectively. These advancements enhance livestock management systems, leading to improved animal welfare, production outcomes, and sustainability in farming practices.
Indexed as
Identifiers
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.