ArticlePloS one2026
Artificial intelligence-based dairy cattle behavior recognition for estrus detection via ensemble fusion of two camera views.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture.Sensors (Basel, Switzerland) · 2026Review
- Estrus detection in dairy cattle: an updated review on strategies and technologies.Frontiers in veterinary science · 2026Review
- Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring cattle behavior plays an important role in improving farm productivity, maintaining animal welfare, and supporting efficient management practices. This study presents a multi-view behavior recognition system that uses synchronized top-view and front-view CCTV footage, combined with deep learning techniques. The system includes four main components: cow identification, behavior classification, identity-behavior association using Intersection-over-Union (IoU), and a decision-level ensemble to combine information from both views. YOLOv8 models are applied separately to each camera angle to detect individual cows and classify six key behaviors: drinking, eating, standing, lying, riding, and chin resting, with the latter two being relevant for estrus detection. The system matches cow identities to their behaviors within each view and then integrates the results to produce a final activity label for each cow.
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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.