Evidence map›Paper›PMID 40541103›Full record

ReviewPoultry science2025

Progress and trends of non-contact detection methods for poultry growth information: A review.

Xin He, Hao Xue, Yuchen Jia, Zongkui Xie, Lihua Li

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Poultry science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

5 authors.

Xin HeCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China; Key Laboratory of Broiler/Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China; Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China.
Hao XueCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China; Key Laboratory of Broiler/Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China; Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China.
Yuchen JiaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China; Key Laboratory of Broiler/Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China; Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China.
Zongkui XieCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China; Key Laboratory of Broiler/Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China; Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China.
Lihua LiCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China; Key Laboratory of Broiler/Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs, Baoding 071000, China; Hebei Provincial Key Laboratory of Livestock and Poultry Breeding Intelligent Equipment and New Energy Utilization, Baoding 071000, China. Electronic address: llh@hebau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increasing global demand for chicken production and welfare, the intelligent and efficient gathering of phenotypic data using non-contact detection procedures will be important for contemporary poultry breeding. This research delineates the pivotal function of non-contact detection technologies in the precise gathering of poultry phenotypic data, emphasizing their application in non-contact monitoring techniques, including sensors and cameras, to improve on-farm chicken observation without disruption. In-depth examination of multifaceted advancements includes the application of convolutional neural networks for monitoring chicken appearance, facilitating recognition of feather coverage and crown color in intensive farming settings. Progress in efficient and precise breeding is outlined, encompassing body size measurements, weight calculation, and external appearance identification in a non-contact environment. Innovations in poultry growth monitoring and relevant case studies are showcased, illustrating how research findings can enhance production and animal welfare. Today, although there are challenges such as complex environments and high equipment costs, combined with future innovative technologies, it is expected to solve these difficulties and improve the efficiency, welfare and sustainability level of poultry farming.

Indexed as

Animal HusbandryChickensAnimalsAnimal WelfareBreedingbreedingnon-contact detectionphenotypingPoultry breeding industry

Identifiers

PMID40541103
PMCPMC12214274

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.