Evidence map›Paper›PMID 40264534›Full record

ReviewJournal of animal science and technology2025

A review of sound-based pig monitoring for enhanced precision production.

Md Nasim Reza, Md Razob Ali, Md Asrakul Haque, Hongbin Jin, Hyunjin Kyoung, Young Kyoung Choi, Gookhwan Kim, Sun-Ok Chung

Abstract readReview
In one paragraph

Review in Journal of animal science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. 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.

Md Nasim RezaDepartment of Agricultural Machinery Engineering, 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
Md Asrakul HaqueDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0002-1351-9712
Hongbin JinDepartment of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0009-0001-0368-9304
Hyunjin KyoungDivision of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0001-5742-5374
Young Kyoung ChoiDAWOON Co., Ltd., Incheon 22847, Korea.ORCID https://orcid.org/0009-0004-9398-5626
Gookhwan KimNational Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54875, Korea.ORCID https://orcid.org/0000-0002-7278-3476
Sun-Ok ChungDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.ORCID https://orcid.org/0000-0001-7629-7224

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pig farming is experiencing significant transformations, driven by technological advancements, which have greatly improved management practices and overall productivity. Sound-based technologies are emerging as a valuable tool in enhancing precision pig farming. This review explores the advancements in sound-based technologies and their role in improving precision pig farming through enhanced monitoring of health, behavior, and environmental conditions. When strategically placed on farms, non-invasive technologies such as microphones and sound sensors can continuously collect data without disturbing the animals, making them highly efficient. Farmers using sound data, can monitor key factors such as respiratory conditions, stress levels, and social behaviors, leading to improved animal welfare and optimized production. Advancements in sensor technology and data analytics have enhanced the capabilities of sound-based precision systems in pig farming. The integration of machine learning and artificial intelligence (AI) is further enhancing the capacity to interpret complex sound patterns, enabling the automated detection of abnormal behaviors or health issues. Moreover, sound-based precision technologies offer solutions for improving environmental sustainability and resource management in pig farming. By continuously monitoring ventilation, feed distribution, and other key factors, these systems optimize resource use, reduce energy consumption, and detect stressors such as heat and poor air quality. The integration of sound technologies with other precision farming tools, such as physiological monitoring sensors and automated feeding systems, further enhances farm management and productivity. However, despite the advantages, challenges remain in terms of low accuracy and high initial costs, and further research is needed to improve specificity across different pig breeds and environmental conditions. Nonetheless, acoustic technologies hold immense promise for pig farming, offering enhanced management, an optimized performance, and improved animal welfare. Continued research can refine these tools and address the challenges, paving the way for a more efficient, profitable, and sustainable future for the industry.

Indexed as

Machine learningPig behaviorPig health monitoringRespiratory diseasesSmart livestock productionSound sensor

Identifiers

PMID40264534
PMCPMC12010234

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.