Development of goat behaviour prediction with accelerometer data: a machine learning and pre-processing approach

The increasing use of accelerometer data for monitoring livestock behaviour in Precision Livestock Farming (PLF) has prompted interest in optimizing machine learning models for real-time applications. This study evaluates the effects of pre-processing factors on predicting goat behaviours using accelerometer data collected in an intensive production environment. A triaxial accelerometer placed on goats’ necks recorded movement data, which was synchronized with video-based ethograms for behavioural annotation. Multiple pre-processing techniques, including filtering, windowing, overlapping and sampling frequency with several feature extraction parameters, were assessed to identify optimal combinations for behaviour classification. Various machine learning algorithms, including classification trees, logistic regression, and multilayer perceptron (MLP) models, were applied to predict eating , walking , and inactive behaviours. Results indicate that some of the pre-processing methods applied could induce inflated evaluation metrics and the importance of the selection of train and test sets. Tree-based classifiers and MLPs demonstrate robust performance, achieving average accuracies above 0.9. Battery performance demonstrate that MLP extends the battery life of the accelerometer device by ∼25 %. These findings highlight the potential of machine learning models in real-time behavioural monitoring to enhance livestock management with goats.