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dc.contributor.author | Hernández, Guillermo | |
dc.contributor.author | González Sánchez, Carlos | |
dc.contributor.author | González Arrieta, Angélica | |
dc.contributor.author | Sánchez Brizuela, Guillermo | |
dc.contributor.author | Fraile Marinero, Juan Carlos | |
dc.date.accessioned | 2024-06-19T11:56:03Z | |
dc.date.available | 2024-06-19T11:56:03Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Sensors, 2024, Vol. 24, Nº. 10, 3157 | es |
dc.identifier.issn | 1424-8220 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/68147 | |
dc.description | Producción Científica | es |
dc.description.abstract | Livestock monitoring is a task traditionally carried out through direct observation by experienced caretakers. By analyzing its behavior, it is possible to predict to a certain degree events that require human action, such as calving. However, this continuous monitoring is in many cases not feasible. In this work, we propose, develop and evaluate the accuracy of intelligent algorithms that operate on data obtained by low-cost sensors to determine the state of the animal in the terms used by the caregivers (grazing, ruminating, walking, etc.). The best results have been obtained using aggregations and averages of the time series with support vector classifiers and tree-based ensembles, reaching accuracies of 57% for the general behavior problem (4 classes) and 85% for the standing behavior problem (2 classes). This is a preliminary step to the realization of event-specific predictions. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Cattle | es |
dc.subject | Ganado vacuno | es |
dc.subject | Animal behavior | es |
dc.subject | Animales - Hábitos y conducta | es |
dc.subject | Extensive livestock | es |
dc.subject | Animales - Cría y explotación | es |
dc.subject | Machine learning | es |
dc.subject | Aprendizaje automático | es |
dc.subject | Monitoring | es |
dc.subject | Sistema de Monitoreo | es |
dc.subject | Wearable device | es |
dc.subject | Detectors | es |
dc.subject | Detectores | |
dc.subject | Technological innovations | |
dc.title | Machine learning-based prediction of cattle activity using sensor-based data | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2024 The authors | es |
dc.identifier.doi | 10.3390/s24103157 | es |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/24/10/3157 | es |
dc.identifier.publicationfirstpage | 3157 | es |
dc.identifier.publicationissue | 10 | es |
dc.identifier.publicationtitle | Sensors | es |
dc.identifier.publicationvolume | 24 | es |
dc.peerreviewed | SI | es |
dc.description.project | Ministerio de Ciencia, Innovación y Universidades, Centro para el Desarrollo Tecnológico y la Innovación (CDTI) y Fondo Europeo de Desarrollo Regional (FEDER)- (grant CIVEX IDI-20180355 and CIVEX IDI-20180354) | es |
dc.identifier.essn | 1424-8220 | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 5102.11 Ganadería | es |
dc.subject.unesco | 3104 Producción Animal | es |
dc.subject.unesco | 1203.25 Diseño de Sistemas Sensores | es |
dc.subject.unesco | 5306.02 Innovación Tecnológica | es |
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