Evidence map›Paper›PMID 38618025›Full record

ReviewJournal of animal science and technology2024

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review.

Md Nasim Reza, Md Razob Ali, Samsuzzaman, Md Shaha Nur Kabir, Md Rejaul Karim, Shahriar Ahmed, Hyunjin Kyoung, Gookhwan Kim, Sun-Ok Chung

Open access · goldAbstract readReview
In one paragraph

Review in Journal of animal science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
8.2field-weighted citation impact, top 1% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Pooled it
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  4. Review
  5. Article
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  7. Review
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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

9 authors at 2 institutions in 2 countries.

Md Nasim RezaDepartment of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0002-7793-400X
Md Razob AliDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0009-0003-7359-8621
SamsuzzamanDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0009-0006-3976-8975
Md Shaha Nur KabirDepartment of Agricultural Industrial Engineering, Faculty of Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh.ORCID https://orcid.org/0000-0003-1685-5292
Md Rejaul KarimDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0002-6301-7446
Shahriar AhmedDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0003-4936-5943
Hyunjin KyoungDivision of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0001-5742-5374
Gookhwan KimNational Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54875, Korea.ORCID https://orcid.org/0000-0002-7278-3476
Sun-Ok ChungDepartment of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0001-7629-7224
Chungnam National University · KRRural Development Administration · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

Indexed as

Computer visionCrush symptom monitoringDisease detectionInfrared thermography (IRT)Remote monitoringSmart farm

Identifiers

PMID38618025
PMCPMC11007457
OpenAlexW4391581186

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.