Show simple item record

dc.contributor.authorRamírez-Morales, Iván
dc.contributor.authorRivero Cebrián, Daniel
dc.contributor.authorFernández-Blanco, Enrique
dc.contributor.authorPazos, A.
dc.date.accessioned2017-04-26T09:00:37Z
dc.date.issued2016-01-02
dc.identifier.citationRamírez Morales I, Rivero Cebrián D, Fernández Blanco E, Pazos Sierra A. Early warning in egg production curves from commercial hens: a SVM approach. Comput Electron Agric. 2016;121:169-179es_ES
dc.identifier.issn0168-1699
dc.identifier.issn1872-7107
dc.identifier.urihttp://hdl.handle.net/2183/18451
dc.description.abstract[Abstract] Artificial Intelligence allows the improvement of our daily life, for instance, speech and handwritten text recognition, real time translation and weather forecasting are common used applications. In the livestock sector, machine learning algorithms have the potential for early detection and warning of problems, which represents a significant milestone in the poultry industry. Production problems generate economic loss that could be avoided by acting in a timely manner. In the current study, training and testing of support vector machines are addressed, for an early detection of problems in the production curve of commercial eggs, using farm’s egg production data of 478,919 laying hens grouped in 24 flocks. Experiments using support vector machines with a 5 k-fold cross-validation were performed at different previous time intervals, to alert with up to 5 days of forecasting interval, whether a flock will experience a problem in production curve. Performance metrics such as accuracy, specificity, sensitivity, and positive predictive value were evaluated, reaching 0-day values of 0.9874, 0.9876, 0.9783 and 0.6518 respectively on unseen data (test-set). The optimal forecasting interval was from zero to three days, performance metrics decreases as the forecasting interval is increased. It should be emphasized that this technique was able to issue an alert a day in advance, achieving an accuracy of 0.9854, a specificity of 0.9865, a sensitivity of 0.9333 and a positive predictive value of 0.6135. This novel application embedded in a computer system of poultry management is able to provide significant improvements in early detection and warning of problems related to the production curve.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttp://doi.org/10.1016/j.compag.2015.12.009es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectEarly warninges_ES
dc.subjectDrop in egg productiones_ES
dc.subjectPoultry managementes_ES
dc.subjectSupport vector machineses_ES
dc.subjectMachine learninges_ES
dc.titleEarly warning in egg production curves from commercial hens: a SVM approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2018-01-02es_ES
dc.date.embargoLift2018-01-02
UDC.journalTitleComputer and Electronics in Agriculturees_ES
UDC.volume121es_ES
UDC.startPage169es_ES
UDC.endPage179es_ES


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record