Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Abstract

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Classification, Categorization, and Algorithms for Articular Cartilage Defects

“…There are other methods to accomplish feature selection which are detailed elsewhere. [77][78][79] Deep learning (DL) is a type of technique for ML in which higher levels of information are extracted using multiple levels, or layers, to transform data into more abstract, composite representations. 80 A major advantage for ML and DL approaches is that they can handle massive amounts of data and factors, making analytical progress a realistic goal for articular cartilage pathology risk assessment, staging and predicting progression of disease, and informing treatment decision making.…”

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

“…Mansbridge et al collected accelerometer and gyroscope signals from sensors attached to the ear and collar of sheep at 16Hz [25]. Various machine learning algorithms were tested using multiple features from the signals.…”

Detection of Steer Defecation Events using an Accelerometer

“…Fukasawa et al (2018) measured the sleeping posture of cattle in lying positions by fitting a three-axis accelerometer on a halter on the middle occipital region. Mansbridge et al (2018) and Barwick et al (2018) classified the eating behaviors in sheep using a three-axis accelerometer worn on the ear. The findings above clearly show that an accelerometer is a powerful tool for distinguishing animal behaviors.…”