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

Aug 3, 2026

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 on goats’ necks recorded movement data synchronized with video-based ethograms for behavioural annotation. Filtering, windowing, overlap and sampling frequency were assessed alongside multiple feature-extraction parameters. Tree-based classifiers and multilayer perceptron models achieved average accuracies above 0.9 and pre-processing choices influenced evaluation metrics and train-test selection.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Precision Livestock Farming and behaviour monitoring

Sensor-based approaches and accelerometer reliability

Device placement and installation considerations

Computers and Electronics in Agriculture 237 (2025) 110701 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal [homepage: www.elsevier.com/locate/compag](homepage: www.elsevier.com/locate/compag) | Development of goat behaviour prediction with accelerometer data: a machine learning and pre-processing approach | | | | | --- | --- | --- | --- | | Daniel Alexander M´endez a,*, Blanca Fajardo a, Sergi Sanjuan b, Jose Manuel Calabuigb, Roger Arnaub, Arantxa Villagr´a c, Salvador Calvet-Sanza, Fernando Estellesa
a Instituto de Ciencia y Tecnología Animal, Universitat Polit`ecnica de Val`encia, Camino de Vera s/n, 46022 Valencia, Spain
b Instituto Universitario de Matem´atica Pura y Aplicada, Universitat Polit`ecnica de Val`encia, Camino de Vera s/n, 46022 Valencia, Valencia, Spain c Centro de Tecnología Animal CITA-IVIA, Polígono La Esperanza, 100. 12400, Segorbe, Castell´on, Spain | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Animal monitoring Multilayer perceptron Tree classifiers Behaviour classification Livestock farming Deep learning | | 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. | | 1. Introduction The key principle of Precision Livestock Farming (PLF) is that fulfilling the needs of animals and crops at the highest level of detail ensures that the needs of farmers, the supply chain, and consumers are also met (Andonovic et al., 2018). Precision monitoring of livestock behaviour has become a valuable tool for evaluating the physiological state of animals. Offering a fast alternative to recognize potential animal welfare issues and health status indicators. However, while these approaches provide an useful preliminary screening tool, they should be considered as an indicator for making a decision, not the decision-maker (Smith et al., 2015). Although the golden standard for behaviour monitoring relies on direct observation, it is time-consuming and labour-intensive. For long monitoring times and commercial applications, direct observation is not only impractical to evaluate animal states, but also potentially incomplete (Riaboff et al., 2019; Sakai et al., 2019). Technological evolution over the past two decades has induced the development of various methods for automatically monitoring animal behaviours; wireless sensor networks and Internet of Things (IoT) technologies have paved the way for implementing monitoring systems on farms. (Andonovicet al., 2018; Cabezas et al., 2022). Thus, non-invasive sensors such as cameras, accelerometers, microphone

FAQs

What problem does the study address in goat behaviour monitoring?

The study targets improving real-time prediction of goat behaviours in Precision Livestock Farming by optimizing how machine learning models use accelerometer signals.

How is accelerometer data collected and annotated for the predictions?

A triaxial accelerometer is placed on goats’ necks to record movement and the data are synchronized with video-based ethograms used for behavioural annotation.

Which machine learning models and behaviours are evaluated?

The work applies classification trees, logistic regression, and multilayer perceptron models to predict eating, walking, and inactive behaviours, focusing on robust classification performance.

Aug 3, 2026

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