ArticleAnimals : an open access journal from MDPI2022
Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools.
Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Computer vision models for precision poultry farming: A narrative review of behavioral and welfare monitoring studies.Poultry science · 2026Review
- Artificial intelligence in precision poultry farming: opportunities, challenges, and future features.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Lightweight multiscale behavior recognition for caged laying hens using an enhanced YOLOv8 framework.Scientific reports · 2026Article
- Aerial monitoring of turkey behavior using unmanned aerial vehicles and computer vision.Poultry science · 2025Article
- Deep Learning Methods for Automatic Identification of Male and Female Chickens in a Cage-Free Flock.Animals : an open access journal from MDPI · 2025Article
- YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation.Sensors (Basel, Switzerland) · 2025Article
- Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust-bathing hens in an experimental aviary. In addition, two YOLO (You Only Look Once) models were compared. YOLOv4-tiny needed about 4.26 h to train for 6000 epochs, compared to about 23.2 h for the full models of YOLOv4. In validation, the performance of the two models in terms of precision, recall, harmonic mean of precision and recall, and mean average precision (mAP) did not differ, while the value of frame per second was lower in YOLOv4 compared to the tiny version (31.35 vs. 208.5). The mAP stands at about 94% for the classification of hens on the floor, while the classification of dust-bathing hens was poor (28.2% in the YOLOv4-tiny compared to 31.6% in YOLOv4). In conclusion, ML successfully identified laying hens on the floor, whereas other PLF tools must be tested for the classification of dust-bathing hens.
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.