Assessment of Preference Behavior of Layer Hens under Different Light Colors and Temperature Environments in Long-Time Footage Using a Computer Vision System

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Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Florida-
Autor(es): dc.creatorKodaira, Vanessa-
Autor(es): dc.creatorSiriani, Allan Lincoln Rodrigues-
Autor(es): dc.creatorMedeiros, Henry Ponti-
Autor(es): dc.creatorDe Moura, Daniella Jorge-
Autor(es): dc.creatorPereira, Danilo Florentino-
Data de aceite: dc.date.accessioned2025-08-21T18:02:14Z-
Data de disponibilização: dc.date.available2025-08-21T18:02:14Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/ani13152426-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309851-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309851-
Descrição: dc.descriptionAs for all birds, the behavior of chickens is largely determined by environmental conditions. In many production systems, light intensity is low and red feather strains have low contrast with the background, making it impossible to use conventional image segmentation techniques. On the other hand, studies of chicken behavior, even when using video camera resources, depend on human vision to extract the information of interest; and in this case, reduced samples are observed, due to the high cost of time and energy. Our work combined the use of advanced object detection techniques using YOLO v4 architecture to locate chickens in low-quality videos, and we automatically extracted information on the location of birds in more than 648 h of footage. We develop an automated system that allows the chickens to transition among three environments with different illuminations equipped with video cameras to monitor the presence of birds in each compartment, and we automatically count the number of birds in each compartment and determine their preference. Our chicken detection algorithm shows a mean average precision of 99.9%, and a manual inspection of the results showed an accuracy of 98.8%. Behavioral analysis results based on bird unrest index and permanence time indicate that chickens tend to prefer white light and disfavor green light, except in the presence of heat stress when no clear preference can be observed. This study demonstrates the potential of using computer vision techniques with low-resolution, low-cost cameras to monitor chickens in low-light conditions.-
Descrição: dc.descriptionGraduate Program in Agricultural Engineering Faculty of Agricultural Engineering Campinas State University, SP-
Descrição: dc.descriptionGraduate Program in Agribusiness and Development School of Science and Engineering São Paulo State University, SP-
Descrição: dc.descriptionDepartment of Agricultural and Biological Engineering University of Florida-
Descrição: dc.descriptionDepartment of Management Development and Technology School of Science and Engineering São Paulo State University, SP-
Descrição: dc.descriptionGraduate Program in Agribusiness and Development School of Science and Engineering São Paulo State University, SP-
Descrição: dc.descriptionDepartment of Management Development and Technology School of Science and Engineering São Paulo State University, SP-
Idioma: dc.languageen-
Relação: dc.relationAnimals-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectenvironmental stress-
Palavras-chave: dc.subjectprecision poultry farming-
Palavras-chave: dc.subjectwelfare-
Palavras-chave: dc.subjectYOLO-
Título: dc.titleAssessment of Preference Behavior of Layer Hens under Different Light Colors and Temperature Environments in Long-Time Footage Using a Computer Vision System-
Tipo de arquivo: dc.typelivro digital-
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