Evidence map›Paper›PMID 39858231›Full record

ArticleAnimals : an open access journal from MDPI2025

Study on the Impact of LDA Preprocessing on Pig Face Identification with SVM.

Hongwen Yan, Yulong Wu, Yifan Bo, Yukuan Han, Gaifeng Ren

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. DGS-YOLO: A Detection Network for Rapid Pig Face Recognition.Animals : an open access journal from MDPI · 2026
    Article
  2. Review
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.

Hongwen YanCollege of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Yulong WuCollege of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Yifan BoCollege of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Yukuan HanCollege of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Gaifeng RenCollege of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Funding

the National Key Research and Development Plan of China;the Shanxi Province Basic Research Program Project (Free Exploration) ;the Doctor Scientific Research Foundation of Shanxi Agricultural University 2016YFD0701801;20210302124523; 202103021224149,;202103021223141;2020BQ14
6 · The paper itself

Abstract

In this study, the implementation of traditional machine learning models in the intelligent management of swine is explored, focusing on the impact of LDA preprocessing on pig facial recognition using an SVM. Through experimental analysis, the kernel functions for two testing protocols, one utilizing an SVM exclusively and the other employing a combination of LDA and an SVM, were identified as polynomial and RBF, both with coefficients of 0.03. Individual identification tests conducted on 10 pigs demonstrated that the enhanced protocol improved identification accuracy from 83.66% to 86.30%. Additionally, the training and testing durations were reduced to 0.7% and 0.3% of the original times, respectively. These findings suggest that LDA preprocessing significantly enhances the efficiency of individual pig identification using an SVM, providing empirical evidence for the deployment of SVM classifiers in mobile and embedded systems.

Indexed as

individual identificationintelligent management of pig breedingLDASVM

Identifiers

PMID39858231
PMCPMC11759145

What OpenQuestion holds

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LicenceCC BY
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.