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Dynamic cattle behavioural classification using supervised ensemble classifiers

Citation

Dutta, R and Smith, D and Rawnsley, R and Bishop-Hurley, G and Hills, J and Timms, G and Henry, D, Dynamic cattle behavioural classification using supervised ensemble classifiers, Computers and Electronics in Agriculture, 111 pp. 18-28. ISSN 0168-1699 (2015) [Refereed Article]

Copyright Statement

Crown copyright 2014

DOI: doi:10.1016/j.compag.2014.12.002

Abstract

In this paper various supervised machine learning techniques were applied to classify cattle behaviour patterns recorded using collar systems with 3-axis accelerometer and magnetometer, fitted to individual dairy cows to infer their physical behaviours. Cattle collar data was collected at the Tasmanian Institute of Agriculture (TIA) Dairy Research Facility in Tasmania. In the first stage of analysis a novel hybrid unsupervised clustering framework, comprised of probabilistic principal component analysis, Fuzzy C Means, and Self Organizing Map network algorithms was developed and used to study the natural structure of the sensor data. Findings from this unsupervised clustering were used to guide the next stage of supervised machine learning. Five major behaviour classes, namely, Grazing, Ruminating, Resting, Walking, and other behaviour were identified for the classification trials. An ensemble of classifiers approach was used to learn models of cow behaviour using sensor data and ground truth behaviour observations acquired from the field. Ensemble classification using bagging, Random Subspace and AdaBoost methods along with conventional supervised classification methods, namely, Binary Tree, Linear Discriminant Analysis classifier, Naive Bayes classifier, k-Nearest Neighbour classifier, and Adaptive Neuro Fuzzy Inference System classifier were compared. The highest average correct classification accuracy of 96% was achieved using the bagging ensemble classification with Tree learner, which had 97% sensitivity, 89% specificity, 89% F1 score and 9% false discovery rate. This study has shown that cattle behaviours can be classified with a high accuracy using supervised machine learning technique. As dairy and beef systems become more intensive, the ability to identify the changes in the behaviours of individual livestock becomes increasingly difficult. Accurate behavioural monitoring through sensors provides a significant potential in providing a mechanism for the early detection and quantitative assessment of animal health issues such a lameness, informing key management events such as the identification of oestrus, or informing changes in supplementary feeding requirements.

Item Details

Item Type:Refereed Article
Keywords:supervised machine learning, dairy cattle, inertial measurement unit, grazing behaviour, cattle tag data analysis, cattle behavioural classification, ensemble machine learning, unsupervised data clustering
Research Division:Agricultural and Veterinary Sciences
Research Group:Animal Production
Research Field:Animal Management
Objective Division:Animal Production and Animal Primary Products
Objective Group:Livestock Raising
Objective Field:Dairy Cattle
Author:Rawnsley, R (Dr Richard Rawnsley)
Author:Hills, J (Dr James Hills)
ID Code:102413
Year Published:2015
Web of Science® Times Cited:18
Deposited By:Tasmanian Institute of Agriculture
Deposited On:2015-08-19
Last Modified:2017-11-07
Downloads:0

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