Vocalization data mining for estimating swine stress conditions
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/11689 |
Resumo: | This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1. |
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Vocalization data mining for estimating swine stress conditionsMineração de dados de vocalização para estimativa de condições de estresse de suínosAnimal welfareBem-estar animalSuínoSwineVocalizaçãoVocalizationThis study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.Este trabalho teve o objetivo de identificar diferenças no padrão de vocalização em função do sexo dos animais e diferentes situações de estresse. Foram utilizados 150 animais machos castrados e 150 fêmeas (linhagem Dalland®), com 100 dias de idade. Os suínos foram submetidos a diferentes situações de estresse: sede (animais sem acesso à água), fome (suínos sem acesso ao alimento), estresse térmico (ITU superior a 74) e BEA (animais com alimento e água, com ITU abaixo de 70). Foram registrados os sinais acústicos a cada 30 minutos, totalizando seis coletas para cada situação de estresse. Posteriormente, os áudios foram analisados pelo software Praat® 5.1.19, gerando um espectro sonoro. Para a determinação das condições de estresse, os dados foram processados no programa computacional WEKA® 3.5, utilizando o algoritmo de árvore de decisão C4.5, conhecido como J48 no ambiente do programa computacional WEKA®, considerando validação cruzada com amostras de 10% (10-fold cross-validation). De acordo com a Árvore de Decisão, o atributo acústico mais importante para a classificação das condições de estresse foi a Intensidade do som (nó raiz). Não foi possível identificar o sexo dos animais pelo registro vocal, utilizando os atributos testados. Foi gerada uma árvore de decisão para reconhecimento de situação de fome, sede e estresse térmico em suínos, a partir de registros da intensidade do som, da frequência de Pitch e da Formante 1.Associação Brasileira de Engenharia Agrícola2016-08-25T13:44:39Z2016-08-25T13:44:39Z2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMOI, M. et al. Vocalization data mining for estimating swine stress conditions. Engenharia Agrícola, Jaboticabal, v. 34, n. 3, p. 445-450, maio/jun. 2014.http://repositorio.ufla.br/jspui/handle/1/11689Engenharia Agrícolareponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAMoi, MartaNaas, Irenilza de AlencarCaldara, Fabiana R.Paz, Ibiara C. de L. AlmeidaGarcia, Rodrigo G.Cordeiro, Alexandra Ferreira da Silvainfo:eu-repo/semantics/openAccesseng2023-05-03T11:26:49Zoai:localhost:1/11689Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T11:26:49Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Vocalization data mining for estimating swine stress conditions Mineração de dados de vocalização para estimativa de condições de estresse de suínos |
title |
Vocalization data mining for estimating swine stress conditions |
spellingShingle |
Vocalization data mining for estimating swine stress conditions Moi, Marta Animal welfare Bem-estar animal Suíno Swine Vocalização Vocalization |
title_short |
Vocalization data mining for estimating swine stress conditions |
title_full |
Vocalization data mining for estimating swine stress conditions |
title_fullStr |
Vocalization data mining for estimating swine stress conditions |
title_full_unstemmed |
Vocalization data mining for estimating swine stress conditions |
title_sort |
Vocalization data mining for estimating swine stress conditions |
author |
Moi, Marta |
author_facet |
Moi, Marta Naas, Irenilza de Alencar Caldara, Fabiana R. Paz, Ibiara C. de L. Almeida Garcia, Rodrigo G. Cordeiro, Alexandra Ferreira da Silva |
author_role |
author |
author2 |
Naas, Irenilza de Alencar Caldara, Fabiana R. Paz, Ibiara C. de L. Almeida Garcia, Rodrigo G. Cordeiro, Alexandra Ferreira da Silva |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Moi, Marta Naas, Irenilza de Alencar Caldara, Fabiana R. Paz, Ibiara C. de L. Almeida Garcia, Rodrigo G. Cordeiro, Alexandra Ferreira da Silva |
dc.subject.por.fl_str_mv |
Animal welfare Bem-estar animal Suíno Swine Vocalização Vocalization |
topic |
Animal welfare Bem-estar animal Suíno Swine Vocalização Vocalization |
description |
This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 2016-08-25T13:44:39Z 2016-08-25T13:44:39Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
MOI, M. et al. Vocalization data mining for estimating swine stress conditions. Engenharia Agrícola, Jaboticabal, v. 34, n. 3, p. 445-450, maio/jun. 2014. http://repositorio.ufla.br/jspui/handle/1/11689 |
identifier_str_mv |
MOI, M. et al. Vocalization data mining for estimating swine stress conditions. Engenharia Agrícola, Jaboticabal, v. 34, n. 3, p. 445-450, maio/jun. 2014. |
url |
http://repositorio.ufla.br/jspui/handle/1/11689 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835186717523968 |