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Decision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logic

dc.contributor.authorDe-Sousa, Karolini Tenffen
dc.contributor.authorDeniz, Matheus [UNESP]
dc.contributor.authorSantos, Maurício Portella dos
dc.contributor.authorKlein, Daniela Regina
dc.contributor.authorVale, Marcos Martinez do
dc.contributor.institutionUniversidade Federal do Paraná (UFPR)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Federal de Santa Maria
dc.date.accessioned2023-07-29T12:49:07Z
dc.date.available2023-07-29T12:49:07Z
dc.date.issued2023-03-01
dc.description.abstractIn this study, we develop an artificial intelligence model to predict the vulnerability of broiler production systems (broilers and facilities) to heat conditions using a fuzzy model approach. The model was designed with a multiple-input and a single-output (MISO) approach (input: physical environment and broilers age; output: degree of vulnerability of broilers system). For the validation of the fuzzy model, two approaches were used: (1) records from the scientific literature and (2) meteorological forecasts. First, we validated the model fuzzy with data from the scientific literature; second, we validate the model with data from meteorological forecasts. Both validation approaches were performed in different scenarios of the thermal environment (comfort, discomfort, and discomfort + low heat exchange), broilers’ age (21–35 days, 25–39 days, and 28–42 days), and relative cooling efficiency (0% inefficient; and 80% efficient). Then, we applied the model to predict the degree of vulnerability of the broiler system with the help of weather forecasts. The recall and precision of the fuzzy model were high (> 0.9) for the thermal comfort and thermal discomfort + low heat exchange scenarios. In contrast, the fuzzy model was moderate agreement (recall 0.45; precision 0.64) for the thermal discomfort scenario compared to the scientific literature. The application of the model with the weather forecast showed the interaction between the physical and biological systems when submitted to a thermal environment challenge. Regardless of the broilers’ age, a high degree of vulnerability was observed in facilities with inefficient cooling system. The fuzzy model developed in this study was efficient to predict the vulnerability of the broiler production system to heat conditions, further, to identify the uncertain conditions associated with broilers’ age, relative humidity, and the relative cooling efficiency of the facilities.en
dc.description.affiliationLaboratório de Inovações Tecnológicas Em Zootecnia Departamento de Zootecnia Universidade Federal Do Paraná, PR
dc.description.affiliationFaculdade de Medicina Veterinária e Zootecnia Universidade Estadual Paulista, SP
dc.description.affiliationPrograma de Pós-Graduação Em Zootecnia Universidade Federal de Santa Maria, RS
dc.description.affiliationUnespFaculdade de Medicina Veterinária e Zootecnia Universidade Estadual Paulista, SP
dc.format.extent475-484
dc.identifierhttp://dx.doi.org/10.1007/s00484-023-02427-1
dc.identifier.citationInternational Journal of Biometeorology, v. 67, n. 3, p. 475-484, 2023.
dc.identifier.doi10.1007/s00484-023-02427-1
dc.identifier.issn1432-1254
dc.identifier.issn0020-7128
dc.identifier.scopus2-s2.0-85146946985
dc.identifier.urihttp://hdl.handle.net/11449/246734
dc.language.isoeng
dc.relation.ispartofInternational Journal of Biometeorology
dc.sourceScopus
dc.subjectArtificial intelligence
dc.subjectDecision-making
dc.subjectExpert system
dc.subjectPrecision livestock farming
dc.titleDecision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logicen
dc.typeArtigo
unesp.author.orcid0000-0003-4743-8547[1]
unesp.author.orcid0000-0001-8079-0070[2]
unesp.author.orcid0000-0001-5030-2363[3]
unesp.author.orcid0000-0002-0429-8268[4]
unesp.author.orcid0000-0002-3010-6602[5]

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