Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data

peer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

Development of a methodological framework for a robust prediction of the main behaviours of dairy cows using a combination of machine learning algorithms on accelerometer data

International audienc