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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
14/06/2023 |
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Data da última atualização: |
22/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
GOMES, J.; ESTEVES, I.; GRACIANO NETO, V. V.; DAVID, J. M. N.; BRAGA, R.; ARBEX, W. A.; KASSAB, M.; OLIVEIRA, R. F. de. |
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Afiliação: |
JONAS GOMES, Universidade Federal de Juiz de Fora; IZAQUE ESTEVES, Universidade Federal de Juiz de Fora; VALDEMAR VICENTE GRACIANO NETO, Universidade Federal de Goiás; JOSÉ MARIA N. DAVID, Universidade Federal de Juiz de Fora; REGINA BRAGA, Universidade Federal de Juiz de Fora; WAGNER ANTONIO ARBEX, CNPGL; MOHAMAD KASSAB, Penn State University; ROBERTO FELÍCIO DE OLIVEIRA, Universidade Estadual de Goiás. |
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Título: |
A scientific software ecosystem architecture for the livestock domain. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Information and Software Technology, v. 160, 107240, 2023. |
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DOI: |
https://doi.org/10.1016/j.infsof.2023.107240 |
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Idioma: |
Inglês |
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Conteúdo: |
Context: In the livestock domain, technologies are developed to sustainably raise animal production. However, the domain is critical, since animals are very sensitive to variables such as temperature and humidity, which can cause diseases and consequent production losses and discomfort. Livestock production systems then demand monitoring, reasoning, and acting on the environment so that the levels of those variables are preserved in pre-established intervals and undesired conditions are predicted, avoided, and mitigated with automated actions. Objective: The main contribution of this article is presenting E-SECO, a software ecosystem platform, and its evolution that encapsulates a new self-adaptive component to tackle animal production decisions, named e-Livestock architecture. Method: Two case studies were conducted involving a real system derived from the E-SECO platform encompassing a Compost Barn production system, i.e., the environment and surrounding technology where bovine milk production takes place. Results: Results showed the effectiveness of E-SECO to (i) abstract disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and accommodate them in a single architecture for that specific domain, (ii) support reuse and derivation of a self-adaptive architecture to support engineering a complex system for a livestock sub-domain (milk production), and (iii) support empirical studies in a real smart farm towards a future transfer of technology to industry. Conclusion: The results showed that the E-SECO platform, which encompasses e-livestock architecture, can support monitoring, reasoning, prediction, and automated actions in a milk production/Compost Barn environment. MenosContext: In the livestock domain, technologies are developed to sustainably raise animal production. However, the domain is critical, since animals are very sensitive to variables such as temperature and humidity, which can cause diseases and consequent production losses and discomfort. Livestock production systems then demand monitoring, reasoning, and acting on the environment so that the levels of those variables are preserved in pre-established intervals and undesired conditions are predicted, avoided, and mitigated with automated actions. Objective: The main contribution of this article is presenting E-SECO, a software ecosystem platform, and its evolution that encapsulates a new self-adaptive component to tackle animal production decisions, named e-Livestock architecture. Method: Two case studies were conducted involving a real system derived from the E-SECO platform encompassing a Compost Barn production system, i.e., the environment and surrounding technology where bovine milk production takes place. Results: Results showed the effectiveness of E-SECO to (i) abstract disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and accommodate them in a single architecture for that specific domain, (ii) support reuse and derivation of a self-adaptive architecture to support engineering a complex system for a livestock sub-domain (milk production), and (iii) support empirical studies in a real smart farm towards a future transfer of technol... Mostrar Tudo |
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Palavras-Chave: |
Compost barn; Dados de sensores; Self-adaptive systems; Sensors data; Software. |
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Thesagro: |
Compostagem; Gado Leiteiro; Pecuária. |
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Thesaurus Nal: |
Computer software; Dairy cattle; Livestock. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 02711naa a2200349 a 4500
001 2154433
005 2023-08-22
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.infsof.2023.107240$2DOI
100 1 $aGOMES, J.
245 $aA scientific software ecosystem architecture for the livestock domain.$h[electronic resource]
260 $c2023
520 $aContext: In the livestock domain, technologies are developed to sustainably raise animal production. However, the domain is critical, since animals are very sensitive to variables such as temperature and humidity, which can cause diseases and consequent production losses and discomfort. Livestock production systems then demand monitoring, reasoning, and acting on the environment so that the levels of those variables are preserved in pre-established intervals and undesired conditions are predicted, avoided, and mitigated with automated actions. Objective: The main contribution of this article is presenting E-SECO, a software ecosystem platform, and its evolution that encapsulates a new self-adaptive component to tackle animal production decisions, named e-Livestock architecture. Method: Two case studies were conducted involving a real system derived from the E-SECO platform encompassing a Compost Barn production system, i.e., the environment and surrounding technology where bovine milk production takes place. Results: Results showed the effectiveness of E-SECO to (i) abstract disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and accommodate them in a single architecture for that specific domain, (ii) support reuse and derivation of a self-adaptive architecture to support engineering a complex system for a livestock sub-domain (milk production), and (iii) support empirical studies in a real smart farm towards a future transfer of technology to industry. Conclusion: The results showed that the E-SECO platform, which encompasses e-livestock architecture, can support monitoring, reasoning, prediction, and automated actions in a milk production/Compost Barn environment.
650 $aComputer software
650 $aDairy cattle
650 $aLivestock
650 $aCompostagem
650 $aGado Leiteiro
650 $aPecuária
653 $aCompost barn
653 $aDados de sensores
653 $aSelf-adaptive systems
653 $aSensors data
653 $aSoftware
700 1 $aESTEVES, I.
700 1 $aGRACIANO NETO, V. V.
700 1 $aDAVID, J. M. N.
700 1 $aBRAGA, R.
700 1 $aARBEX, W. A.
700 1 $aKASSAB, M.
700 1 $aOLIVEIRA, R. F. de
773 $tInformation and Software Technology$gv. 160, 107240, 2023.
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| 14. |  | BRAGA, R. M. O ecótipo Cavalo Lavradeiro. In: RIBEIRO, N. L.; MEDEIROS, G. R. de.; . GOMES, I. L. C.; NASCIMENTO, G. V. do. V.; SANTOS, S. G. C. G. dos. Cavalos e jumentos do Brasil: raças e ecótipos. Campina Grande, PB: Instituto Nacional do Semiárido, 2023. p. 79-108.| Tipo: Capítulo em Livro Técnico-Científico |
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