Using Thermal Signature to Evaluate Heat Stress Levels in Laying Hens with a Machine-Learning-Based Classifier

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorSolis, Isaac Lembi-
Autor(es): dc.creatorde Oliveira-Boreli, Fernanda Paes-
Autor(es): dc.creatorde Sousa, Rafael Vieira-
Autor(es): dc.creatorMartello, Luciane Silva-
Autor(es): dc.creatorPereira, Danilo Florentino-
Data de aceite: dc.date.accessioned2025-08-21T20:49:26Z-
Data de disponibilização: dc.date.available2025-08-21T20:49:26Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/ani14131996-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/297727-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/297727-
Descrição: dc.descriptionInfrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens’ bodies were cut out. Rectal temperature was used to label each infrared thermography data as “Danger” or “Normal”, and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.-
Descrição: dc.descriptionBusiness Administration Undergraduate School of Sciences and Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionGraduate Program in Agribusiness and Development School of Sciences and Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionFaculty of Animal Science and Food Engineering (FZEA) Department of Biosystems Engineering University of São Paulo (USP), SP-
Descrição: dc.descriptionSchool of Sciences and Engineering Department of Management Development and Technology São Paulo State University (UNESP), SP-
Descrição: dc.descriptionBusiness Administration Undergraduate School of Sciences and Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionGraduate Program in Agribusiness and Development School of Sciences and Engineering São Paulo State University (UNESP), SP-
Descrição: dc.descriptionSchool of Sciences and Engineering Department of Management Development and Technology São Paulo State University (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationAnimals-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectanimal welfare-
Palavras-chave: dc.subjectdata mining-
Palavras-chave: dc.subjectfeatherless surface temperature-
Palavras-chave: dc.subjectinfrared thermography-
Palavras-chave: dc.subjectsupervised learning-
Título: dc.titleUsing Thermal Signature to Evaluate Heat Stress Levels in Laying Hens with a Machine-Learning-Based Classifier-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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