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Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume
This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into t...
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Published in: | Animals (Basel) 2021-12, Vol.11 (12), p.3595 |
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description | This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815,
< 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R
of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor. |
doi_str_mv | 10.3390/ani11123595 |
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< 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R
of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.</description><identifier>ISSN: 2076-2615</identifier><identifier>EISSN: 2076-2615</identifier><identifier>DOI: 10.3390/ani11123595</identifier><identifier>PMID: 34944370</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>3D image ; Animal welfare ; Body weight ; carcass composition ; Carcasses ; Composition ; Correlation analysis ; Digital cameras ; Evaluation ; Image acquisition ; Image processing ; lambs ; Leg ; leg volume ; Light ; Microsoft Kinect ; Muscles ; Sensors ; Software ; Standard deviation ; Statistical analysis ; Three dimensional imaging</subject><ispartof>Animals (Basel), 2021-12, Vol.11 (12), p.3595</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c475t-ceca0a433e8247089ce716dd3065956c430569289a634d49d9ef68fc14efcd263</citedby><cites>FETCH-LOGICAL-c475t-ceca0a433e8247089ce716dd3065956c430569289a634d49d9ef68fc14efcd263</cites><orcidid>0000-0003-3581-5595 ; 0000-0003-0482-5459 ; 0000-0002-8390-4907</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2612726052/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2612726052?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,44566,53766,53768,74869</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34944370$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Silva, Severiano R</creatorcontrib><creatorcontrib>Almeida, Mariana</creatorcontrib><creatorcontrib>Condotta, Isabella</creatorcontrib><creatorcontrib>Arantes, André</creatorcontrib><creatorcontrib>Guedes, Cristina</creatorcontrib><creatorcontrib>Santos, Virgínia</creatorcontrib><title>Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume</title><title>Animals (Basel)</title><addtitle>Animals (Basel)</addtitle><description>This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815,
< 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R
of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.</description><subject>3D image</subject><subject>Animal welfare</subject><subject>Body weight</subject><subject>carcass composition</subject><subject>Carcasses</subject><subject>Composition</subject><subject>Correlation analysis</subject><subject>Digital cameras</subject><subject>Evaluation</subject><subject>Image acquisition</subject><subject>Image processing</subject><subject>lambs</subject><subject>Leg</subject><subject>leg volume</subject><subject>Light</subject><subject>Microsoft Kinect</subject><subject>Muscles</subject><subject>Sensors</subject><subject>Software</subject><subject>Standard deviation</subject><subject>Statistical analysis</subject><subject>Three dimensional imaging</subject><issn>2076-2615</issn><issn>2076-2615</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdks9rFDEUgAdRbKk9eZeAF0FW83smF6GsVhcX9GC9hjfJm9ksM5M1mS30vzfbrWVrDsnjvY-Pl-RV1WtGPwhh6EeYAmOMC2XUs-qc01ovuGbq-Ul8Vl3mvKVl1UowxV5WZ0IaKUVNz6t4lTPmHKaezBsk1wg5tGEI8x2JHbm5L3wPE7qZiM9kNUKPmcyR_EzoQ0muQ78pO4wtWUJycLCRZRx3MYc5xIl0KY5kjT35HYf9iK-qFx0MGS8fzovq5vrLr-W3xfrH19Xyar1wslbzwqEDClIIbLisaWMc1kx7L6guN9VOCqq04Y0BLaSXxhvsdNM5JrFznmtxUa2OXh9ha3cpjJDubIRg7xMx9RbSHNyAtgWlvGpco6iQWBsQrVOya6BlXjjE4vp0dO327Yje4TQnGJ5In1amsLF9vLWNNg2lsgjePQhS_LPHPNsxZIfDABPGfbbllySXvOamoG__Q7dxn6byVAeqIJoqXqj3R8qlmHPC7rEZRu1hLuzJXBT6zWn_j-y_KRB_AcXjstM</recordid><startdate>20211219</startdate><enddate>20211219</enddate><creator>Silva, Severiano R</creator><creator>Almeida, Mariana</creator><creator>Condotta, Isabella</creator><creator>Arantes, André</creator><creator>Guedes, Cristina</creator><creator>Santos, Virgínia</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3581-5595</orcidid><orcidid>https://orcid.org/0000-0003-0482-5459</orcidid><orcidid>https://orcid.org/0000-0002-8390-4907</orcidid></search><sort><creationdate>20211219</creationdate><title>Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume</title><author>Silva, Severiano R ; Almeida, Mariana ; Condotta, Isabella ; Arantes, André ; Guedes, Cristina ; Santos, Virgínia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c475t-ceca0a433e8247089ce716dd3065956c430569289a634d49d9ef68fc14efcd263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>3D image</topic><topic>Animal welfare</topic><topic>Body weight</topic><topic>carcass composition</topic><topic>Carcasses</topic><topic>Composition</topic><topic>Correlation analysis</topic><topic>Digital cameras</topic><topic>Evaluation</topic><topic>Image acquisition</topic><topic>Image processing</topic><topic>lambs</topic><topic>Leg</topic><topic>leg volume</topic><topic>Light</topic><topic>Microsoft Kinect</topic><topic>Muscles</topic><topic>Sensors</topic><topic>Software</topic><topic>Standard deviation</topic><topic>Statistical analysis</topic><topic>Three dimensional imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Silva, Severiano R</creatorcontrib><creatorcontrib>Almeida, Mariana</creatorcontrib><creatorcontrib>Condotta, Isabella</creatorcontrib><creatorcontrib>Arantes, André</creatorcontrib><creatorcontrib>Guedes, Cristina</creatorcontrib><creatorcontrib>Santos, Virgínia</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Animals (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Silva, Severiano R</au><au>Almeida, Mariana</au><au>Condotta, Isabella</au><au>Arantes, André</au><au>Guedes, Cristina</au><au>Santos, Virgínia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume</atitle><jtitle>Animals (Basel)</jtitle><addtitle>Animals (Basel)</addtitle><date>2021-12-19</date><risdate>2021</risdate><volume>11</volume><issue>12</issue><spage>3595</spage><pages>3595-</pages><issn>2076-2615</issn><eissn>2076-2615</eissn><abstract>This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. 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< 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R
of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>34944370</pmid><doi>10.3390/ani11123595</doi><orcidid>https://orcid.org/0000-0003-3581-5595</orcidid><orcidid>https://orcid.org/0000-0003-0482-5459</orcidid><orcidid>https://orcid.org/0000-0002-8390-4907</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 3D image Animal welfare Body weight carcass composition Carcasses Composition Correlation analysis Digital cameras Evaluation Image acquisition Image processing lambs Leg leg volume Light Microsoft Kinect Muscles Sensors Software Standard deviation Statistical analysis Three dimensional imaging |
title | Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume |
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