Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/128783
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Ismail, Shahid | en_US |
dc.contributor.author | Diaz, Moises | en_US |
dc.contributor.author | Carmona Duarte, María Cristina | en_US |
dc.contributor.author | Vilar, Jose Manuel | en_US |
dc.contributor.author | Ferrer Ballester, Miguel Ángel | en_US |
dc.date.accessioned | 2024-02-03T23:23:25Z | - |
dc.date.available | 2024-02-03T23:23:25Z | - |
dc.date.issued | 2024 | en_US |
dc.identifier.issn | 0168-1699 | en_US |
dc.identifier.other | Scopus | - |
dc.identifier.uri | http://hdl.handle.net/10553/128783 | - |
dc.description.abstract | Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness assessment. However, the major limitation in their development is the unavailability of a public database, as most existing ones are either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB, a multi-sensor database which was built with data from 43 dairy cows. Cows were monitored using smart watches during their normal daily routine. The uniqueness of the database lies in its data collection environment, sampling methodology, detailed sensor information, and the applications used for data conversion and storage, which ensure transparency and replicability. This data transparency makes CowScreeningDB a valuable and objectively comparable resource for further development of techniques for lameness detection for dairy cows. In addition to publicly sharing the database, we present a machine learning technique which classifies cows as healthy or lame by using raw sensory data. To facilitate fair comparisons with state-of-the-art methods, we introduce a novel benchmark. Combining the database, the machine learning technique and the benchmark validate our major objective, which is to establish the relationship between sensor data and lameness. The developed technique reports an average accuracy of 77 % for the best case scenario and presents perspectives for further development. By introducing this framework which encompasses the database, the classification algorithm and the benchmark, we significantly reduce subjectively in lameness assessment. This contribution to lameness detection fosters innovation in the field and promotes transparent, reproducible research in the pursuit of more effective management of dairy cow lameness. Implications: Lameness detection is one of the main tasks in dairy systems, given its importance in the production ambit. However, the data used during detection is generally either held privately or sold commercially. In this study, we create a multi-sensor database (CowScreeningDB), which can be used for lameness. Because we have made the database public1 and free of charge for research purposes, it should act as a benchmark allowing to objectively compare techniques put forth to deal with lameness. We also provide details of the sampling system used, comprised of hardware and a baseline classification algorithm. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Computers and Electronics in Agriculture | en_US |
dc.source | Computers and Electronics in Agriculture [ISSN 0168-1699], v. 216, 108500, (Enero 2024) | en_US |
dc.subject | 120312 Bancos de datos | en_US |
dc.subject | 240111 Patología animal | en_US |
dc.subject.other | Cow | en_US |
dc.subject.other | Dairy | en_US |
dc.subject.other | Lameness | en_US |
dc.subject.other | Machine Learning | en_US |
dc.subject.other | Public Database | en_US |
dc.subject.other | Support Vector Machine | en_US |
dc.title | CowScreeningDB: A public benchmark database for lameness detection in dairy cows | en_US |
dc.type | info:eu-repo/semantics/Article | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.compag.2023.108500 | en_US |
dc.identifier.scopus | 85179488657 | - |
dc.contributor.orcid | NO DATA | - |
dc.contributor.orcid | NO DATA | - |
dc.contributor.orcid | NO DATA | - |
dc.contributor.orcid | NO DATA | - |
dc.contributor.orcid | NO DATA | - |
dc.contributor.authorscopusid | 57201735463 | - |
dc.contributor.authorscopusid | 58552611900 | - |
dc.contributor.authorscopusid | 57217055027 | - |
dc.contributor.authorscopusid | 7005533720 | - |
dc.contributor.authorscopusid | 55636321172 | - |
dc.relation.volume | 216 | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.description.numberofpages | 13 | en_US |
dc.utils.revision | Sí | en_US |
dc.date.coverdate | Enero 2024 | en_US |
dc.identifier.ulpgc | Sí | en_US |
dc.contributor.buulpgc | BU-TEL | en_US |
dc.description.sjr | 1,735 | |
dc.description.jcr | 8,3 | |
dc.description.sjrq | Q1 | |
dc.description.jcrq | Q1 | |
dc.description.scie | SCIE | |
dc.description.miaricds | 11,0 | |
item.fulltext | Con texto completo | - |
item.grantfulltext | open | - |
crisitem.author.dept | GIR IDeTIC: División de Procesado Digital de Señales | - |
crisitem.author.dept | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.dept | Departamento de Física | - |
crisitem.author.dept | GIR IDeTIC: División de Procesado Digital de Señales | - |
crisitem.author.dept | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.dept | GIR IUIBS: Medicina Veterinaria e Investigación Terapéutica | - |
crisitem.author.dept | IU de Investigaciones Biomédicas y Sanitarias | - |
crisitem.author.dept | Departamento de Patología Animal, Producción Animal, Bromatología y Tecnología de Los Alimentos | - |
crisitem.author.dept | GIR IDeTIC: División de Procesado Digital de Señales | - |
crisitem.author.dept | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.dept | Departamento de Señales y Comunicaciones | - |
crisitem.author.orcid | 0000-0003-3878-3867 | - |
crisitem.author.orcid | 0000-0002-4441-6652 | - |
crisitem.author.orcid | 0000-0002-2060-2274 | - |
crisitem.author.orcid | 0000-0002-2924-1225 | - |
crisitem.author.parentorg | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.parentorg | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.parentorg | IU de Investigaciones Biomédicas y Sanitarias | - |
crisitem.author.parentorg | IU para el Desarrollo Tecnológico y la Innovación | - |
crisitem.author.fullName | Díaz Cabrera, Moisés | - |
crisitem.author.fullName | Carmona Duarte, María Cristina | - |
crisitem.author.fullName | Vilar Guereño, José Manuel | - |
crisitem.author.fullName | Ferrer Ballester, Miguel Ángel | - |
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