dc.contributor.author | Michelena, Álvaro | |
dc.contributor.author | Díaz-Longueira, Antonio | |
dc.contributor.author | Novais, Paulo | |
dc.contributor.author | Simić, Dragan | |
dc.contributor.author | Fontenla-Romero, Óscar | |
dc.contributor.author | Calvo-Rolle, José Luis | |
dc.date.accessioned | 2024-12-18T06:47:39Z | |
dc.date.available | 2024-12-18T06:47:39Z | |
dc.date.issued | 2025-02-14 | |
dc.identifier.citation | Á. Michelena, A. Díaz-Longueira, P. Novais, D. Simić, Ó. Fontenla-Romero, J.L. Calvo-Rolle, Comparative analysis of unsupervised anomaly detection techniques for heat detection in dairy cattle, Neurocomputing 618 (2025) 129088. https://doi.org/10.1016/j.neucom.2024.129088. | es_ES |
dc.identifier.issn | 1872-8286 | |
dc.identifier.uri | http://hdl.handle.net/2183/40540 | |
dc.description.abstract | [Abstract] Population growth has increased the demand for meat and dairy products, making livestock, especially cattle, key to meeting this demand. This has led to an increase in herd size, complicating efficient herd management. To meet this challenge, innovative technologies, such as monitoring collars, have been developed to improve individual animal management.
This research work evaluates and compares three unsupervised anomaly detection methods to identify estrus in dairy cows from intensive farms, based on daily activity data recorded by a commercial monitoring collar. Data from two different dairy farms have been used and the results have been compared by evaluating the behavior both individually and at herd level. The results obtained show a good performance of the selected techniques in the individual animal models. Thus, this research demonstrates that these techniques can be very useful tools in farm management, providing valuable information, improving productivity and, consequently, increasing the economic performance of the farm. | es_ES |
dc.description.sponsorship | Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference FPU21/00932.
Antonio Díaz-Longueira’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to Ph.D. (http://gain.xunta.gal), under the “Axudas á etapa predoutoral” grant with reference: ED481A-2023-072.
CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021–27 operational program (Ref. ED431G 2023/01).
Xunta de Galicia. Grants for the consolidation and structuring of competitive research units, GPC (ED431B 2023/49). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2023/49 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A-2023-072 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A-2023-072 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MUNI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/FPU21%2F00932/ES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.neucom.2024.129088 | es_ES |
dc.rights | Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Cattle behavior | es_ES |
dc.subject | Smart collars | es_ES |
dc.subject | DBSCAN | es_ES |
dc.subject | Local outlier factor | es_ES |
dc.subject | Isolation forest | es_ES |
dc.title | Comparative Analysis of Unsupervised Anomaly Detection Techniques for Heat Detection in Dairy Cattle | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Neurocomputing | es_ES |
UDC.volume | 618 | es_ES |
UDC.issue | 129088 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 12 | es_ES |
dc.identifier.doi | https://doi.org/10.1016/j.neucom.2024.129088 | |
UDC.coleccion | Investigación | es_ES |
UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | es_ES |
UDC.departamento | Enxeñaría Industrial | es_ES |
UDC.grupoInv | Ciencia e Técnica Cibernética (CTC) | es_ES |
UDC.grupoInv | Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) | es_ES |
UDC.institutoCentro | CITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación | es_ES |