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Registro Completo |
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Biblioteca(s): |
Embrapa Agrossilvipastoril. |
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Data corrente: |
10/02/2024 |
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Data da última atualização: |
10/02/2024 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
BENICIO, L. M.; XAVIER, D. B.; LIMA, I. B. G. de; PEREIRA, D. H.; CONDOTTA, I. C. F. da S.; LOPES, L. B. |
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Afiliação: |
LUANA MARIA BENICIO, UNIVERSIDADE DE ILLINOIS; DIEGO BATISTA XAVIER, CPAMT; ITALO BRAZ GONÇALVES DE LIMA, UNIVERSIDADE DE ILLINOIS; DALTON HENRIQUE PEREIRA, UNIVERSIDADE FEDERAL DE MATO GROSSO; ISABELLA CARDOSO FERREIRA DA SILVA CONDOTTA, UNIVERSIDADE DE ILLINOIS; LUCIANO BASTOS LOPES, CPAMT. |
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Título: |
Individual cattle face recognition through computer vision. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: JORNADA CIENTÍFICA DA EMBRAPA AGROSSILVIPASTORIL, 12., 2023. Sinop. Resumos... Brasília, DF: Embrapa, 2023. p. 24. |
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Série: |
(Embrapa Agrossilvipastoril. Eventos Técnicos & Científicos, 1) |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Identifying a single animal in a herd allows for individual management and association, and traceability over time of each animal. Over the years, methods have been adopted to identify animals individually, such as using ear tags, visible ear tags, tattoos, and radio frequency identification devices (RFID). However, a disadvantage of this system is that the tool requires much work to configure (identification and tracking system), and the cost to install and replace the RFID tags must be considered. Systems based on image processing can be considered alternatives for individual identification of farm animals, as it is a non-invasive tool, is easy to apply, and can be adopted in real-time. Furthermore, it is possible to monitor more than one animal simultaneously individually. Several authors have used images to identify animals by face images, using the individual pattern as an ID. This work aims to analyze to develop an automatic individual identification of Nellore cattle through facial recognition using computer vision techniques. Facial images were collected from 30 Nellore males during weighing using the Intel RealSense camera. Then, only the face region of the animals was selected automatically, and an image classification model, using Yolo architecture version 8, was developed. Preliminary results showed an accuracy of 95% when correlating the animal's face with the identification number without the need to use identification ear tags. Thus, the proposed model presents potential for field applicability in individual animal recognition. The following steps of this research will include a more significant number of animals in the model and field testing to validate the methodology. MenosAbstract: Identifying a single animal in a herd allows for individual management and association, and traceability over time of each animal. Over the years, methods have been adopted to identify animals individually, such as using ear tags, visible ear tags, tattoos, and radio frequency identification devices (RFID). However, a disadvantage of this system is that the tool requires much work to configure (identification and tracking system), and the cost to install and replace the RFID tags must be considered. Systems based on image processing can be considered alternatives for individual identification of farm animals, as it is a non-invasive tool, is easy to apply, and can be adopted in real-time. Furthermore, it is possible to monitor more than one animal simultaneously individually. Several authors have used images to identify animals by face images, using the individual pattern as an ID. This work aims to analyze to develop an automatic individual identification of Nellore cattle through facial recognition using computer vision techniques. Facial images were collected from 30 Nellore males during weighing using the Intel RealSense camera. Then, only the face region of the animals was selected automatically, and an image classification model, using Yolo architecture version 8, was developed. Preliminary results showed an accuracy of 95% when correlating the animal's face with the identification number without the need to use identification ear tags. Thus, the proposed mod... Mostrar Tudo |
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Thesaurus Nal: |
Animal identification; Computer vision; Face. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161943/1/2023-cpamt-lbl-individual-cattle-face-recognition-through-computer-vision-p24.pdf
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Marc: |
LEADER 02475nam a2200217 a 4500
001 2161943
005 2024-02-10
008 2023 bl uuuu u00u1 u #d
100 1 $aBENICIO, L. M.
245 $aIndividual cattle face recognition through computer vision.$h[electronic resource]
260 $aIn: JORNADA CIENTÍFICA DA EMBRAPA AGROSSILVIPASTORIL, 12., 2023. Sinop. Resumos... Brasília, DF: Embrapa, 2023. p. 24.$c2023
490 $a(Embrapa Agrossilvipastoril. Eventos Técnicos & Científicos, 1)
520 $aAbstract: Identifying a single animal in a herd allows for individual management and association, and traceability over time of each animal. Over the years, methods have been adopted to identify animals individually, such as using ear tags, visible ear tags, tattoos, and radio frequency identification devices (RFID). However, a disadvantage of this system is that the tool requires much work to configure (identification and tracking system), and the cost to install and replace the RFID tags must be considered. Systems based on image processing can be considered alternatives for individual identification of farm animals, as it is a non-invasive tool, is easy to apply, and can be adopted in real-time. Furthermore, it is possible to monitor more than one animal simultaneously individually. Several authors have used images to identify animals by face images, using the individual pattern as an ID. This work aims to analyze to develop an automatic individual identification of Nellore cattle through facial recognition using computer vision techniques. Facial images were collected from 30 Nellore males during weighing using the Intel RealSense camera. Then, only the face region of the animals was selected automatically, and an image classification model, using Yolo architecture version 8, was developed. Preliminary results showed an accuracy of 95% when correlating the animal's face with the identification number without the need to use identification ear tags. Thus, the proposed model presents potential for field applicability in individual animal recognition. The following steps of this research will include a more significant number of animals in the model and field testing to validate the methodology.
650 $aAnimal identification
650 $aComputer vision
650 $aFace
700 1 $aXAVIER, D. B.
700 1 $aLIMA, I. B. G. de
700 1 $aPEREIRA, D. H.
700 1 $aCONDOTTA, I. C. F. da S.
700 1 $aLOPES, L. B.
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Registro original: |
Embrapa Agrossilvipastoril (CPAMT) |
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Registro Completo
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
03/03/2017 |
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Data da última atualização: |
03/03/2017 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
SANTOS, R. C. dos; DUARTE, M. de M. F.; SILVA, M. DE F. C. DA; BRAZ, L. C. C.; SILVA, C. R. C. DA; CAVALCANTI, J. J. V.; LIMA, L. M. de; MARTINS, E. S.; MONNERAT, R. |
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Afiliação: |
ROSEANE CAVALCANTI DOS SANTOS, CNPA; MARÍLIA de MACÊDO FREIRE DUARTE, UEPB; MARIA DE FÁTIMA CAETANO DA SILVA, UEPB; LUANA CAMILLA CORDEIRO BRAZ, UFCG; CARLIANE REBECA COELHO DA SILVA, RENORBIO, UFRPE; JOSE JAIME VASCONCELOS CAVALCANTI, CNPA; LIZIANE MARIA DE LIMA, CNPA; ÉRICA SOARES MARTINS, CENARGEN; ROSE MONNERAT, CENARGEN. |
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Título: |
Initial assessment of GM cotton resistant to cotton boll weevil based on feeding bioassays and Elisa. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: ENCONTRO DE GENÉTICA DO NORDESTE, 21., 2016, Recife. Anais... Ribeirão Preto: SBG; Recife: UFPE: UFRPE: UPE, 2016. |
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Idioma: |
Inglês |
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Conteúdo: |
The boll weevil (Anthonomus grandis) is the main pest of cotton crop due to cause serious damages to reproductive structures, affecting directly the yield. The effective control is done through chemical insecticides, which substantially increase management costs. Control via transgene sis is a promising strategy and less harmful to the environment, especially by using Cry proteins, derived from endophytic Bt bacteria. In 2015, the biotech team from Embrapa introduced a cry 10-construction into cotton plants, isolated from a Bt strain that showed low DL50 in boll weevil feeding bioassays (7.12 mg/mL). In order to test the toxicity of Cry proteins were conducted in laboratory bioassays feeding with boll weevil and ELISA assays. Three hundred and sixty plants T0 were grown in green house and further were evaluated in feeding bioassays and ELISA tests, carried out in Entomology and Biotech Labs, at Embrapa Algodão (Campina Grande, PB). |
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Palavras-Chave: |
Melhoramento de plantas. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/157030/1/Initial-assessment-of-GM-cotton.pdf
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Marc: |
LEADER 01704nam a2200217 a 4500
001 2066081
005 2017-03-03
008 2016 bl uuuu u00u1 u #d
100 1 $aSANTOS, R. C. dos
245 $aInitial assessment of GM cotton resistant to cotton boll weevil based on feeding bioassays and Elisa.$h[electronic resource]
260 $aIn: ENCONTRO DE GENÉTICA DO NORDESTE, 21., 2016, Recife. Anais... Ribeirão Preto: SBG; Recife: UFPE: UFRPE: UPE$c2016
520 $aThe boll weevil (Anthonomus grandis) is the main pest of cotton crop due to cause serious damages to reproductive structures, affecting directly the yield. The effective control is done through chemical insecticides, which substantially increase management costs. Control via transgene sis is a promising strategy and less harmful to the environment, especially by using Cry proteins, derived from endophytic Bt bacteria. In 2015, the biotech team from Embrapa introduced a cry 10-construction into cotton plants, isolated from a Bt strain that showed low DL50 in boll weevil feeding bioassays (7.12 mg/mL). In order to test the toxicity of Cry proteins were conducted in laboratory bioassays feeding with boll weevil and ELISA assays. Three hundred and sixty plants T0 were grown in green house and further were evaluated in feeding bioassays and ELISA tests, carried out in Entomology and Biotech Labs, at Embrapa Algodão (Campina Grande, PB).
653 $aMelhoramento de plantas
700 1 $aDUARTE, M. de M. F.
700 1 $aSILVA, M. DE F. C. DA
700 1 $aBRAZ, L. C. C.
700 1 $aSILVA, C. R. C. DA
700 1 $aCAVALCANTI, J. J. V.
700 1 $aLIMA, L. M. de
700 1 $aMARTINS, E. S.
700 1 $aMONNERAT, R.
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Registro original: |
Embrapa Algodão (CNPA) |
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