Evidence map›Paper›PMID 40075918›Full record

ReviewAnimals : an open access journal from MDPI2025

Monitoring Pig Structural Soundness and Body Weight in Pork Production Systems Using Computer Vision Approaches.

Ryan Jeon, Caleb Rykaczewski, Thomas Williams, William Harrington, James E Kinder, Mark Trotter

Abstract readReview
In one paragraph

Review 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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Ryan JeonInteger Technologies LLC, 1556 Main Street, Suite 200, Columbia, SC 29201, USA.
Caleb RykaczewskiDepartment of Animal Sciences, College of Food, Agricultural, and Environmental Sciences, The Ohio State University, Columbus, OH 43210, USA.
Thomas WilliamsInstitute of Future Farming Systems, School of Health, Medical, and Applied Sciences, CQUniversity, Rockhampton, QLD 4701, Australia.ORCID 0000-0001-7205-2270
William HarringtonCollege of Business, Law, and Governance, James Cook University, Townsville QLD 4811, Australia.ORCID 0000-0003-2366-0020
James E KinderDepartment of Animal Sciences, College of Food, Agricultural, and Environmental Sciences, The Ohio State University, Columbus, OH 43210, USA.ORCID 0000-0002-0085-3367
Mark TrotterInstitute of Future Farming Systems, School of Health, Medical, and Applied Sciences, CQUniversity, Rockhampton, QLD 4701, Australia.ORCID 0000-0001-6363-2193

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the global demand for products from food-producing animals increases with greater household economic capacity, there is an increased emphasis on the development of precision technologies for monitoring the health, product production, and wellbeing of these animals. The present review focuses on pork production. Using these systems is advantageous for enhancing pork production efficiency when trained personnel utilize these technologies to full capacity and have objective, automated, and uninterrupted streams of data collection. While these systems have great potential for revolutionizing food animal production, the nascent stage of computer vision in precision technology has precluded its integration into traditional agricultural practices and systems. In this review paper, there is a focus on the need to (1) evaluate the performance and effective use of computer vision technologies to collect and evaluate reliable data from pork production enterprises; and (2) focus on the current state of sensor-based animal management using a data fusion approach to monitor pig health/performance. Many of these technologies are in various stages of development; therefore, these technologies have not been integrated into pork production or other food animal producing systems. Even though the focus of this review article is on the utilization of these technologies in pork production systems, these technologies are relevant in other food animal production systems, particularly dairy and poultry production. Therefore, we describe an approach that emphasizes the important need for computational capacity and speed, edge computing, data storage and transmission, and maintaining connectivity in rural settings.

Indexed as

computer visionpork productionprecision technologiesskeletal structureweight

Identifiers

PMID40075918
PMCPMC11898204

What OpenQuestion holds

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LicenceCC BY
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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.