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dc.contributor.authorRamírez-Morales, Iván
dc.contributor.authorFernández-Blanco, Enrique
dc.contributor.authorRivero, Daniel
dc.contributor.authorPazos, A.
dc.date.accessioned2017-11-20T09:02:34Z
dc.date.issued2017-10-17
dc.identifier.citationRamírez-Morales I, Fernández-Blanco E, Rivero D, Pazos A. Automated early detection of drops in commercial egg production using neural networks. Br Poult Sci. 2017;58(6):739-747es_ES
dc.identifier.issn0007-1668
dc.identifier.issn1466-1799
dc.identifier.urihttp://hdl.handle.net/2183/19770
dc.description.abstract[Abstract] 1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of cost-sensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.es_ES
dc.description.sponsorshipGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049es_ES
dc.language.isoenges_ES
dc.publisherTaylor & Francises_ES
dc.relation.urihttp://dx.doi.org/10.1080/00071668.2017.1379051es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rightsThis is an accepted manuscript of an article published by Taylor & Francis in British Poultry Science on 2017, avaliable online at Taylor & Francis Online.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectFarming systemses_ES
dc.subjectLaying henses_ES
dc.subjectMachine learninges_ES
dc.subjectModellinges_ES
dc.subjectProduction dropses_ES
dc.titleAutomated early detection of drops in commercial egg production using neural networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2018-10-17es_ES
dc.date.embargoLift2018-10-17
UDC.journalTitleBritish Poultry Sciencees_ES
UDC.volume58es_ES
UDC.issue6es_ES
UDC.startPage739es_ES
UDC.endPage747es_ES


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