VTechWorks staff will be away for the winter holidays starting Tuesday, December 24, 2024, through Wednesday, January 1, 2025, and will not be replying to requests during this time. Thank you for your patience, and happy holidays!
 

Forecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor camera

Simple item page
dc.contributor.authorYu, Haipengen
dc.contributor.authorLee, Kihoen
dc.contributor.authorMorota, Gotaen
dc.contributor.departmentAnimal and Poultry Sciencesen
dc.contributor.departmentCenter for Advanced Innovation in Agricultureen
dc.date.accessioned2021-08-24T13:42:57Zen
dc.date.available2021-08-24T13:42:57Zen
dc.date.issued2021-01-01en
dc.date.updated2021-08-24T13:42:54Zen
dc.description.abstractAverage daily gain is an indicator of the growth rate, feed efficiency, and current health status of livestock species including pigs. Continuous monitoring of daily gain in pigs aids producers to optimize their growth performance while ensuring animal welfare and sustainability, such as reducing stress reactions and feed waste. Computer vision has been used to predict live body weight from video images without direct handling of the pig. In most studies, videos were taken while pigs were immobilized at a weighing station or feeding area to facilitate data collection. An alternative approach is to capture videos while pigs are allowed to move freely within their own housing environment, which can be easily applied to the production system as no special imaging station needs to be established. The objective of this study was to establish a computer vision system by collecting RGB-D videos to capture top-view red, green, and blue (RGB) and depth images of nonrestrained, growing pigs to predict their body weight over time. Over a period of 38 d, eight growers were video recorded for approximately 3 min/d, at the rate of six frames per second, and manually weighed using an electronic scale. An image-processing pipeline in Python using OpenCV was developed to process the images. Specifically, each pig within the RGB frame was segmented by a thresholding algorithm, and the contour of the pig was identified to extract its length and width. The height of a pig was estimated from the depth images captured by the infrared depth sensor. Quality control included removing pigs that were touching the fence and sitting, as well as those showing extremely distorted shape or motion blur owing to their frequent movement. Fitting all of the morphological image descriptors simultaneously in linear mixed models yielded prediction coefficients of determination of 0.72-0.98, 0.65-0.95, 0.51-0.94, and 0.49-0.93 for 1-, 2-, 3-, and 4-d ahead forecasting, respectively, of body weight in time series cross-validation. Based on the results, we conclude that our RGB-D sensor-based imaging system coupled with the Python image-processing pipeline could potentially provide an effective approach to predict the live body weight of nonrestrained pigs from images.en
dc.description.versionPublished versionen
dc.format.extentPages txab006en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1093/tas/txab006en
dc.identifier.eissn2573-2102en
dc.identifier.issn2573-2102en
dc.identifier.issue1en
dc.identifier.orcidMorota, Gota [0000-0002-3567-6911]en
dc.identifier.otherPMC7906448 (pmc)en
dc.identifier.othertxab006 (PII)en
dc.identifier.pmid33659861 (pubmed)en
dc.identifier.urihttp://hdl.handle.net/10919/104698en
dc.identifier.volume5en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/33659861en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectbody weighten
dc.subjectcomputer visionen
dc.subjectimage analysisen
dc.subjectswineen
dc.titleForecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor cameraen
dc.title.serialTranslational Animal Scienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
dcterms.dateAccepted2021-01-12en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Animal and Poultry Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Forecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor camera.pdf
Size:
999.2 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

All Faculty Deposits
Scholarly Works, Center for Advanced Innovation in Agriculture
Scholarly Works, School of Animal Sciences