Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks
•Individual recognition of Pantanal cattle through the application of Convolutional Neural Networks (CNN).•Annotadet dataset with 27,849 images of Pantanal cattle, extracted from 212 videos.•Experimental results show that the architectural models used in the research achieved 99.86% accuracy. The ob...
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| Veröffentlicht in: | Computers and electronics in agriculture 2020-08, Vol.175, p.105548, Article 105548 |
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| creator | Weber, Fabricio de Lima Weber, Vanessa Aparecida de Moraes Menezes, Geazy Vilharva Oliveira Junior, Adair da Silva Alves, Daniela Arestides de Oliveira, Marcus Vinicius Morais Matsubara, Edson Takashi Pistori, Hemerson Abreu, Urbano Gomes Pinto de |
| description | •Individual recognition of Pantanal cattle through the application of Convolutional Neural Networks (CNN).•Annotadet dataset with 27,849 images of Pantanal cattle, extracted from 212 videos.•Experimental results show that the architectural models used in the research achieved 99.86% accuracy.
The objective of this paper is to provide recognition for Pantaneira cattle breed using Convolutional Neural Networks (CNN). Fifty-one animals from the Aquidauana Pantaneira cattle Center (NUBOPAN) were studied. The center is located in the Midwest region of Brazil. Four monitoring cameras were distributed in the fences and took 27,849 images of Pantaneira cattle breed using different angles and positions. The following three CNN architectures were used for the experiment: DenseNet-201, Resnet50 and Inception-Resnet-V. All networks were submitted to 10-fold stratified cross-validation over 50 epochs. The results showed an accuracy of 99% in all networks, which is encouraging for future research. |
| doi_str_mv | 10.1016/j.compag.2020.105548 |
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The objective of this paper is to provide recognition for Pantaneira cattle breed using Convolutional Neural Networks (CNN). Fifty-one animals from the Aquidauana Pantaneira cattle Center (NUBOPAN) were studied. The center is located in the Midwest region of Brazil. Four monitoring cameras were distributed in the fences and took 27,849 images of Pantaneira cattle breed using different angles and positions. The following three CNN architectures were used for the experiment: DenseNet-201, Resnet50 and Inception-Resnet-V. All networks were submitted to 10-fold stratified cross-validation over 50 epochs. The results showed an accuracy of 99% in all networks, which is encouraging for future research.</description><identifier>ISSN: 0168-1699</identifier><identifier>EISSN: 1872-7107</identifier><identifier>DOI: 10.1016/j.compag.2020.105548</identifier><language>eng</language><publisher>OXFORD: Elsevier B.V</publisher><subject>Agriculture ; Agriculture, Multidisciplinary ; Artificial neural networks ; Cattle ; CNN ; Computer Science ; Computer Science, Interdisciplinary Applications ; Computer vision ; Individual cattle recognition ; Life Sciences & Biomedicine ; Neural networks ; Pantaneira cattle ; Recognition ; Science & Technology ; Technology</subject><ispartof>Computers and electronics in agriculture, 2020-08, Vol.175, p.105548, Article 105548</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright Elsevier BV Aug 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>27</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000552020100020</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c334t-64bd21b0d20fdc53c51433d62611f123854cb2e716842c06f19950d2eee8dbef3</citedby><cites>FETCH-LOGICAL-c334t-64bd21b0d20fdc53c51433d62611f123854cb2e716842c06f19950d2eee8dbef3</cites><orcidid>0000-0002-7523-1183 ; 0000-0001-8181-760X ; 0000-0001-9598-701X ; 0000-0002-8870-4509 ; 0000-0002-3388-6050 ; 0000-0002-4471-0886 ; 0000-0002-6688-369X ; 0000-0002-7057-5091</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.compag.2020.105548$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,28253,46000</link.rule.ids></links><search><creatorcontrib>Weber, Fabricio de Lima</creatorcontrib><creatorcontrib>Weber, Vanessa Aparecida de Moraes</creatorcontrib><creatorcontrib>Menezes, Geazy Vilharva</creatorcontrib><creatorcontrib>Oliveira Junior, Adair da Silva</creatorcontrib><creatorcontrib>Alves, Daniela Arestides</creatorcontrib><creatorcontrib>de Oliveira, Marcus Vinicius Morais</creatorcontrib><creatorcontrib>Matsubara, Edson Takashi</creatorcontrib><creatorcontrib>Pistori, Hemerson</creatorcontrib><creatorcontrib>Abreu, Urbano Gomes Pinto de</creatorcontrib><title>Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks</title><title>Computers and electronics in agriculture</title><addtitle>COMPUT ELECTRON AGR</addtitle><description>•Individual recognition of Pantanal cattle through the application of Convolutional Neural Networks (CNN).•Annotadet dataset with 27,849 images of Pantanal cattle, extracted from 212 videos.•Experimental results show that the architectural models used in the research achieved 99.86% accuracy.
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Weber, Vanessa Aparecida de Moraes ; Menezes, Geazy Vilharva ; Oliveira Junior, Adair da Silva ; Alves, Daniela Arestides ; de Oliveira, Marcus Vinicius Morais ; Matsubara, Edson Takashi ; Pistori, Hemerson ; Abreu, Urbano Gomes Pinto de</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-64bd21b0d20fdc53c51433d62611f123854cb2e716842c06f19950d2eee8dbef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Agriculture</topic><topic>Agriculture, Multidisciplinary</topic><topic>Artificial neural networks</topic><topic>Cattle</topic><topic>CNN</topic><topic>Computer Science</topic><topic>Computer Science, Interdisciplinary Applications</topic><topic>Computer vision</topic><topic>Individual cattle recognition</topic><topic>Life Sciences & Biomedicine</topic><topic>Neural networks</topic><topic>Pantaneira cattle</topic><topic>Recognition</topic><topic>Science & Technology</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Weber, Fabricio de Lima</creatorcontrib><creatorcontrib>Weber, Vanessa Aparecida de Moraes</creatorcontrib><creatorcontrib>Menezes, Geazy Vilharva</creatorcontrib><creatorcontrib>Oliveira Junior, Adair da Silva</creatorcontrib><creatorcontrib>Alves, Daniela Arestides</creatorcontrib><creatorcontrib>de Oliveira, Marcus Vinicius Morais</creatorcontrib><creatorcontrib>Matsubara, Edson Takashi</creatorcontrib><creatorcontrib>Pistori, Hemerson</creatorcontrib><creatorcontrib>Abreu, Urbano Gomes Pinto de</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers and electronics in agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Weber, Fabricio de Lima</au><au>Weber, Vanessa Aparecida de Moraes</au><au>Menezes, Geazy Vilharva</au><au>Oliveira Junior, Adair da Silva</au><au>Alves, Daniela Arestides</au><au>de Oliveira, Marcus Vinicius Morais</au><au>Matsubara, Edson Takashi</au><au>Pistori, Hemerson</au><au>Abreu, Urbano Gomes Pinto de</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks</atitle><jtitle>Computers and electronics in agriculture</jtitle><stitle>COMPUT ELECTRON AGR</stitle><date>2020-08</date><risdate>2020</risdate><volume>175</volume><spage>105548</spage><pages>105548-</pages><artnum>105548</artnum><issn>0168-1699</issn><eissn>1872-7107</eissn><abstract>•Individual recognition of Pantanal cattle through the application of Convolutional Neural Networks (CNN).•Annotadet dataset with 27,849 images of Pantanal cattle, extracted from 212 videos.•Experimental results show that the architectural models used in the research achieved 99.86% accuracy.
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| subjects | Agriculture Agriculture, Multidisciplinary Artificial neural networks Cattle CNN Computer Science Computer Science, Interdisciplinary Applications Computer vision Individual cattle recognition Life Sciences & Biomedicine Neural networks Pantaneira cattle Recognition Science & Technology Technology |
| title | Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks |
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