Identificador persistente para citar o vincular este elemento: https://accedacris.ulpgc.es/handle/10553/139730
Título: Deep learning for lameness level detection in dairy cows
Autores/as: Ismail, Shahid
Diaz, Moises 
Ferrer, Miguel A. 
Clasificación UNESCO: 33 Ciencias tecnológicas
Palabras clave: Artificial Intelligence Lameness Detection
Deep Learning Application
Voting System Application Of Artificial Intelligence
Fecha de publicación: 2025
Publicación seriada: Engineering Applications of Artificial Intelligence 
Resumen: Lameness detection using raw sensor data is a very challenging task, as the data are devoid of specific information regarding predictors such as gait distribution, weight among legs, etc. We have addressed this challenge using a deep learning technique, named LLP-Cow (Lameness level predictor for Cow), which is an application of artificial intelligence (AI). For objective comparison, LLP-Cow is validated using CowScreeningDB, an unbalanced public dataset composed of sensor data. This dataset is recorded during the normal life of dairy cows. Hence, LLP-Cow models the normal behaviour of cows and consists of feature extraction, application-specific deep network and a voting system. The technique presented is able to model the behaviour of a cow for both binary and multiclass classification. The precision and specificity reported by our technique stand at 0.94 and 0.98 for multiclass and 0.91 and 0.90 for binary protocols for the best case scenario. Moreover, F1 measure, Matthews correlation coefficient and Kappa are 0.94, 0.91, and 0.91, respectively. The technique introduced provides a margin for human intervention through the use of a voting system at the classification stage. The technique presented is therefore an implemented AI system for cow lameness detection that offers room for exploration in terms of real time implementation.
URI: https://accedacris.ulpgc.es/handle/10553/139730
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2025.110611
Fuente: Engineering Applications of Artificial Intelligence[ISSN 0952-1976],v. 151, (Julio 2025)
Colección:Artículos
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