Evidence map›Paper›PMID 41895022›Full record

ArticlePoultry science2026

Characterizing mating behaviour in broiler breeders via a vision based deep learning model.

M Jaihuni, Y Zhao, H Gan, J Moyle, T Tabler, H Qi, X Zhu, S C N Hewage

Abstract read
In one paragraph

Article in Poultry science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

M JaihuniDepartment of Animal Science, University of Tennessee, Knoxville, TN 37996, USA.
Y ZhaoDepartment of Animal Science, University of Tennessee, Knoxville, TN 37996, USA. Electronic address: yzhao@utk.edu.
H GanDepartment of Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, TN 37996, USA.
J MoyleUniversity of Maryland Extension, College Park, MD 20742, USA.
T TablerDepartment of Animal Science, University of Tennessee, Knoxville, TN 37996, USA.
H QiDepartment of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA.
X ZhuResearch Computing Support, Office of Innovative Technologies, University of Tennessee, Knoxville, TN 37996, USA.
S C N HewageDepartment of Animal Science, University of Tennessee, Knoxville, TN 37996, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Broiler breeder mating behaviour consists of rapidly performed sub-actions that determine the success of sperm transfer critical for egg fertility. It includes rooster mounting the hen followed by coordinated tail movements that enable cloacal contact. This study aimed to develop a vision-based deep learning (DL) model for automatically detecting the mounting phase, the period between mounting and decoupling during mating, and to evaluate its features over time. An experiment was conducted with four pens each containing 10 hens (Cobb 500F) and 1 rooster (Cobb MX strains) for four and a half months. Two cameras were stationed at each pen continuously recording broiler breeders daily. The DL model, YOLOv8, was fine-tuned and tested to detect two behavioural classes, mounting and non-mounting, with a dataset consisting of 2420 instances and further validated with independent dataset; the final model was applied on the experimental dataset to detect daily mounting phase events. Additionally, the welfare indicators footpad dermatitis (FPD) and gait score (GS), along with live weight, were periodically evaluated in broiler breeders and maintained within the Cobb recommended limits. Finally, a linear mixed model (LMM) was applied to evaluate individual and covariate effects of age, welfare and weight variables on the mounting phase results. The final model achieved 91.0 % and 90.0 % test accuracies in detecting mounting and non-mounting behaviours by roosters, respectively. The model detection results revealed an average 13.5 ± 4.2 mounts per rooster per day, with each one lasting 5.3 ± 0.8 seconds and happening with 70.6 ± 28.9 minutes inter-mounting interval. On average, 68.2 % of mounting phases were carried out with observable rooster and hen tail movements. Mountings that entailed tail movements lasted longer than the ones with none. The peak mounting activity was during the 16:00-20:00 followed by the 07:00-09:00 hours. Heavier roosters (p < 0.0001) and hens with poorer gait conditions (p = 0.0179) were less likely to engage in or sustain during mating. These findings may enhance understanding of the mating temporal dynamics and facilitate egg fertility analyses.

Indexed as

Animal HusbandryChickensDeep LearningSexual Behavior, AnimalAnimalsFemaleMaleBroiler BreederFertilityMating behaviourMounting phaseTail movement

Identifiers

PMID41895022
PMCPMC13054000

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.