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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; 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; ISABELLA CARDOSO FERREIRA DA SILVA CONDOTTA, UNIVERSIDADE DE ILLINOIS; LUCIANO BASTOS LOPES, CPAMT. |
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Título: |
Automated body score assessment for dairy cows using depth image processing. |
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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. 25. |
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Série: |
(Embrapa Agrossilvipastoril. Eventos Técnicos & Científicos, 1) |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The body condition score (BCS) is the most helpful system available to dairy farmers to assess the nutritional status of cows, as it indicates the body fat levels of the animals. Body condition assessment of dairy cows can be used as a management tool for feeding, reproduction, health, and longevity of the herd. However, observers' assessment of body condition can be considered a subjective method, which may contain biases and disagreements between assessors, making the observational method less accurate. Thus, more objective and automatic systems using image processing can be considered alternatives for obtaining the body score. Imaging allows for obtaining the score of more than one animal at the same time, besides being possible to obtain such information remotely, being considered a non-invasive and non-stressful system for the herd, being possible to adopt such a tool in real-time. Therefore, the present work aimed to develop an automatic system for obtaining the body score of dairy cows through depth images. An Intel RealSense depth sensor was installed above the corridor of the passage of the animals to the electronic scale to collect images of forty animals. Then, an image-processing algorithm and extraction of animal surface area, projected volume, average animal height, and minimum and maximum body lengths were developed. Subsequently, the extracted data were correlated with the body score, obtained manually by observers during image collection, through a machine learning model. In that model, the body dimensions were used as the input source and the body score as the output. The score observed ranged from 2 to 4.5. The preliminary results of this study revealed an R2 of 0.75 and an average error of 4%. This R2 value can be explained by the small number of images used for each score. However, it should be emphasized that the initial model presented a relatively low error compared to the manual classification. Thus, it is possible to confirm the tool's potential to correctly and automatically evaluate the body score of the animals, especially after the insertion of the next steps for the improvement and performance of the model and inserting a more significant number of animals into the analysis. MenosAbstract: The body condition score (BCS) is the most helpful system available to dairy farmers to assess the nutritional status of cows, as it indicates the body fat levels of the animals. Body condition assessment of dairy cows can be used as a management tool for feeding, reproduction, health, and longevity of the herd. However, observers' assessment of body condition can be considered a subjective method, which may contain biases and disagreements between assessors, making the observational method less accurate. Thus, more objective and automatic systems using image processing can be considered alternatives for obtaining the body score. Imaging allows for obtaining the score of more than one animal at the same time, besides being possible to obtain such information remotely, being considered a non-invasive and non-stressful system for the herd, being possible to adopt such a tool in real-time. Therefore, the present work aimed to develop an automatic system for obtaining the body score of dairy cows through depth images. An Intel RealSense depth sensor was installed above the corridor of the passage of the animals to the electronic scale to collect images of forty animals. Then, an image-processing algorithm and extraction of animal surface area, projected volume, average animal height, and minimum and maximum body lengths were developed. Subsequently, the extracted data were correlated with the body score, obtained manually by observers during image collection, through a... Mostrar Tudo |
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Palavras-Chave: |
Machine learning. |
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Thesaurus Nal: |
Body condition; Image processors. |
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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/1161944/1/2023-cpamt-lbl-automated-body-score-assessment-dairy-cows-using-depth-image-processing-p25.pdf
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Marc: |
LEADER 02996nam a2200205 a 4500
001 2161944
005 2024-02-10
008 2023 bl uuuu u00u1 u #d
100 1 $aBENICIO, L. M.
245 $aAutomated body score assessment for dairy cows using depth image processing.$h[electronic resource]
260 $aIn: JORNADA CIENTÍFICA DA EMBRAPA AGROSSILVIPASTORIL, 12., 2023. Sinop. Resumos... Brasília, DF: Embrapa, 2023. p. 25.$c2023
490 $a(Embrapa Agrossilvipastoril. Eventos Técnicos & Científicos, 1)
520 $aAbstract: The body condition score (BCS) is the most helpful system available to dairy farmers to assess the nutritional status of cows, as it indicates the body fat levels of the animals. Body condition assessment of dairy cows can be used as a management tool for feeding, reproduction, health, and longevity of the herd. However, observers' assessment of body condition can be considered a subjective method, which may contain biases and disagreements between assessors, making the observational method less accurate. Thus, more objective and automatic systems using image processing can be considered alternatives for obtaining the body score. Imaging allows for obtaining the score of more than one animal at the same time, besides being possible to obtain such information remotely, being considered a non-invasive and non-stressful system for the herd, being possible to adopt such a tool in real-time. Therefore, the present work aimed to develop an automatic system for obtaining the body score of dairy cows through depth images. An Intel RealSense depth sensor was installed above the corridor of the passage of the animals to the electronic scale to collect images of forty animals. Then, an image-processing algorithm and extraction of animal surface area, projected volume, average animal height, and minimum and maximum body lengths were developed. Subsequently, the extracted data were correlated with the body score, obtained manually by observers during image collection, through a machine learning model. In that model, the body dimensions were used as the input source and the body score as the output. The score observed ranged from 2 to 4.5. The preliminary results of this study revealed an R2 of 0.75 and an average error of 4%. This R2 value can be explained by the small number of images used for each score. However, it should be emphasized that the initial model presented a relatively low error compared to the manual classification. Thus, it is possible to confirm the tool's potential to correctly and automatically evaluate the body score of the animals, especially after the insertion of the next steps for the improvement and performance of the model and inserting a more significant number of animals into the analysis.
650 $aBody condition
650 $aImage processors
653 $aMachine learning
700 1 $aXAVIER, D. B.
700 1 $aLIMA, I. B. G. de
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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| Registros recuperados : 4 | |
| 1. |  | MAGALHAES, M. P.; GOMES, F. S.; SILVA, L. F. M.; CABRAL, L. M. Reducao da carga microbiana da agua de coco verde por microfiltracao. In: CONGRESSO BRASILEIRO DE CIENCIA E TECNOLOGIA DE ALIMENTOS, 17., 2000, Fortaleza, CE. Livro de resumos... Fortaleza: SBCTA, 2000. v.1, p.4.150, 2000.| Biblioteca(s): Embrapa Agroindústria de Alimentos. |
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| 3. | | SCHMIDT, M. J.; PY-DANIEL, A. R.; MORAES, C. de P.; VALLE, R. B. M.; CAROMANO, C. F.; TEIXEIRA, W. G.; BARBOSA, C. A.; FONSECA, J. A.; MAGALHÃES, M. P.; SANTOS, D. S. do C.; SILVA, R. da S. e; GUAPINDAIA, V. L.; MORAES, B.; LIMA, H. P.; NEVES, E. G.; HECKENBERGER, M. J. Dark earths and the human built landscape in Amazonia: a widespread pattern of anthrosol formation. Journal of Archaeological Science, v. 42, p. 152-165, Feb. 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Solos. |
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| 4. | | LOMBARDO, U.; ARROYO-KALIN, M.; SCHMIDT, M.; HUISMAN, H.; LIMA, H. P.; MORAES, M. de P.; NEVES, E. G.; CLEMENT, C. R.; FONSECA, J. A. da; ALMEIDA, F. O. de; ALHO, C. F. B. V.; RAMSEY, C. B.; BROWN, G. G.; CAVALLINI, M. S.; COSTA, M. L. da; CUNHA, L.; ANJOS, L. H. C. dos; DENEVAN, W. M.; FAUSTO, C.; CAROMANO, C. F.; FONTANA, A.; FRANCHETTO, B.; GLASER, B.; HECKENBERGER, M. J.; HECHT, S.; HONORATO, V.; JAROSCH, K. A.; JUNQUEIRA, A. B.; KATER, T.; TAMANAHA, E. K.; KUYPER, T. W.; LEHMANN, J.; MADELLA, M.; MAEZUMI, S. Y.; CASCON, L. M.; MAYLE, F. E.; MCKEY, D.; MORAES, B.; MORCOTE-RÍOS, G.; BARBOSA, C. A. P.; MAGALHÃES, M. P.; PRESTES-CARNEIRO, G.; PUGLIESE, F.; PUPIM, F. N.; RACZKA, M. F.; PY-DANIEL, A. R.; ROCHA, B. C. da; RODRIGUES, L.; ROSTAIN, S.; MACEDO, R. S.; SHOCK, M. P.; SPRAFKE, T.; BASSI, F. S.; VALLE, R.; VIDAL-TORRADO, P.; VILLAGRÁN, X. S.; WATLING, J.; WEBER, S. L.; TEIXEIRA, W. G. Evidence confirms an anthropic origin of Amazonian Dark Earths. Nature Communications, v. 13, n. 3444, 2022. 6 p.| Tipo: Nota Técnica/Nota Científica |
| Biblioteca(s): Embrapa Florestas; Embrapa Solos. |
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| Registros recuperados : 4 | |
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