Accelerometers-based position and time interval comparisons for predicting the behaviors of young bulls housed in a feedlot system

• Main Author: Watanabe, Rafael Nakamura [UNESP]
• Publication Date: 2024
• Other Authors: Romanzini, Eliéder Prates [UNESP], Bernardes, Priscila Arrigucci, Rodrigues, Julia Lisboa [UNESP], Alves do Val, Guilherme [UNESP], Silva, Matheus Mello [UNESP], Fernandes, Márcia Helena Machado da Rocha [UNESP], Caetano, Sabrina Luzia [UNESP], Ramos, Salvador Boccaletti [UNESP], Reis, Ricardo Andrade [UNESP], Munari, Danísio Prado [UNESP]
• Format: Article
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1016/j.atech.2024.100542; https://hdl.handle.net/11449/298520
• topic: Machine learning; Multilayer perceptron; Precise livestock management; Random forest; Support vector machine

Animal behavior monitoring is an important tool for animal production. This behavior monitoring strategy can indicate the well-being and health of animals, which can lead to better productive performance. This study aimed to assess the most effective accelerometer attachment position (on either the halter or a neck collar) and data transmission time intervals (ranging from 6 to 600 s) for predicting behavioral patterns, including water and food intake frequencies, as well as other activities in young beef cattle bulls within a feedlot system. A range of machine learning algorithms were applied to satisfy the aims of the study, including the random forest, support vector machine, multilayer perceptron, and naive Bayes classifier algorithms. All studied models produced high performance metrics (above 0.90) when using both attachment positions, except for the models built using the naive Bayes classifier. Therefore, coupling accelerometers with collars is a more viable alternative for use on animals, as doing so is easier than applying accelerometers to halters. Utilizing a dataset with more observations (i.e., shorter time intervals) did not result in considerable improvements in the performance metrics of the trained models. Therefore, using datasets with fewer observations is more advantageous, as it can lead to decreased computational and temporal demands for model training, in addition to saving the battery of the device considered in this study.