Evidence map›Paper›PMID 36139285›Full record

ArticleAnimals : an open access journal from MDPI2022

A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network.

Yufei Ren, Yikang Huang, Yichen Wang, Shijie Zhang, Hao Qu, Jie Ma, Longhe Wang, Lin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. An Advanced Chicken Face Detection Network Based on GAN and MAE.Animals : an open access journal from MDPI · 2022
    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

8 authors.

Yufei RenCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Yikang HuangCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Yichen WangCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Shijie ZhangCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Hao QuInstitute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Jie MaInstitute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Longhe WangNational Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), Beijing 100083, China.
Lin LiCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

Funding

Key-Area Research and Development Program of Guangdong Province 2022B0202100002
6 · The paper itself

Abstract

Thanks to the boom of computer vision techniques and artificial intelligence algorithms, it is more available to achieve artificial rearing for animals in real production scenarios. Improving the accuracy of chicken day-age detection is one of the instances, which is of great importance for chicken rearing. To solve this problem, we proposed an attention encoder structure to extract chicken image features, trying to improve the detection accuracy. To cope with the imbalance of the dataset, various data enhancement schemes such as Cutout, CutMix, and MixUp were proposed to verify the effectiveness of the proposed attention encoder. This paper put the structure into various mainstream CNN networks for comparison and multiple ablation experiments. The final experimental results show that by applying the attention encoder structure, ResNet-50 can improve the accuracy of chicken age detection to 95.2%. Finally, this paper also designed a complete image acquisition system for chicken houses and a detection application configured for mobile devices.

Indexed as

artificial intelligence applicationattention encoderchick day-age classificationconvolutional neural networkedge computationprecision livestock

Identifiers

PMID36139285
PMCPMC9495009

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.