David Henry

Dynamic cattle behavioural classification using supervised ensemble classifiers

Cattle behavioural classification using cattle tag and supervised ensemble classifiers.Unsupervised hybrid clustering used to study inherent natural grouping in data set.Best classification accuracy was 96% using the bagging ensemble with Tree learner.A mechanism for the early detection and quantitative assessment of animal health. 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.

The influence of NaCl salinity and hypoxia on aspects of growth in Trifolium species

The effects of salinity and hypoxia on growth, nutritive value, and ion relations were evaluated in 38 species of Trifolium and 3 check legume species (Trifolium fragiferum, Trifolium michelianum, and Medicago sativa) under glasshouse conditions, with the aim of identifying species that may be suitable for saline and/or waterlogged conditions. In the first set of experiments, plants were grown hydroponically at four NaCl concentrations (0, 40, 80, and 160 mm NaCl) and harvested after exposure to these treatments for 4 weeks. NaCl concentrations up to 160 mm reduced dry matter production in most species; however, there were differences in salt tolerance among species, with T. argutum, T. diffusum, T. hybridum, and T. ornithopodioides performing well under the saline conditions (dry matter production was reduced by less than 20%). Concentrations of Na+ and Cl− in the shoots increased with increasing salinity levels, and species again differed in their capacity to limit the uptake of these ions. Dry matter digestibility at 0 mm ranged from 49.8% (T. palaestinum) to 74.0% (T. vesiculosum) and decreased with increasing NaCl concentrations. A second set of experiments evaluated the tolerance of Trifolium species to hypoxic conditions in the glasshouse. Shoot growth, and to a lesser extent root growth, were reduced in all Trifolium species when plants were exposed to stagnant, non-aerated conditions for 28 days, but T. michelianum, T. resupinatum, T. squamosum, T. nigrescens, T. ornithopodioides, T. salmoneum, and T. fragiferum were the least affected species. All species acclimated to the oxygen-depleted conditions by increasing the gas-filled porosity in the roots. This study has provided information that will assist in the identification of forage species for saline and/or waterlogged areas.

Bag of Class Posteriors, a new multivariate time series classifier applied to animal behaviour identification

A new multi-scale time series classifier is proposed using class posterior estimates.The classifier infers a large set of animal behaviour using motion based time series.The proposed classifier outperforms benchmark classifiers by between 43% and 77%.The proposed classifier is found to be more efficient than the Bag of Features model. In this paper, two new multivariate time series classifiers are introduced as the Bag of Class Posteriors (BOCP) and the Bag of Class Posterior with Ordering (BOCPO). The models propose a new multi-scale feature representation where the class posterior estimates of contiguous local patterns are aggregated over longer time scales. The models are employed as part of an animal behaviour monitoring system that are comprised of sensors, which are fitted to the animals, and a classifier that translates sensor data into knowledge of the animals behaviour.Animal monitoring systems are commonly developed to infer a small number of behaviours with relevance to a specific application. To investigate if a standard monitoring system with an Inertial Measurement Unit (IMU) can be reused for different management applications, a set of ten cattle behaviours relevant to different management applications were classified with the proposed models. Results indicate that the multi-scale BOCP and BOCPO models were far more capable of classifying the cow behaviours offering a 43% to 77% improvement over benchmark time interval classifiers with fixed time resolution. In addition, the BOCPO model was shown to offer a far more efficient feature representation than the related multi-scale Bag of Features (BOF) classifier (up to 200 times smaller) making it better suited to deploy upon monitoring devices fitted to animals in the field.

Detecting heat events in dairy cows using accelerometers and unsupervised learning

We developed a heat detection algorithm for pasture-based dairy cows.Our algorithm uses data from accelerometer attached to the cow collars.We present the overall accuracy of 82-100% with 100% sensitivity. This study was conducted to investigate the detection of heat events in pasture-based dairy cows fitted with on-animal sensors using unsupervised learning. Accelerometer data from the cow collars were used to identify increased activity levels in cows associated with recorded heat events. Time series data from the accelerometers were first segmented into windows before features were extracted. K-means clustering algorithm was then applied across the windows for grouping. The groups were labelled in terms of their activity intensity: high, medium and low. An activity index level (AIxL) was then derived from a count of activity intensity labels over time. Change detection techniques were then applied on AIxL to find very high activity events. Detected events in AIxL were compared with recorded heat events and observed significant associations between the increased activities through high AIxL values and the observed heat events. We achieved overall accuracy of 82-100% with 100% sensitivity when change detection technique is applied to activity index level.

Behavior classification of cows fitted with motion collars: Decomposing multi-class classification into a set of binary problems

Precision management systems for livestock offer the potential to monitor and manage animals on an individual basis. A key component of these sensor based systems are the analytical models that automatically translate sensor data into different behavioral categories. A new methodology was proposed for multi-class behavior modeling based upon the “one-vs-all” framework. This methodology differs from the standard approach to behavior classification where a single classifier is trained to discriminate between multiple behaviors. Instead a set of binary classifiers are trained to each discriminate one of the behavior classes against a combined class of all the remaining behaviors. The confidence scores from the set of binary classifiers are then combined to generate a behavioral estimate. The performance of this new modeling approach is validated across a study involving 24 Holstein-Friesian dairy cows that were each fitted with an Inertial Measurement Unit (IMU) on a collar upon their neck. Five general classes of cow behavior grazing, walking, ruminating, resting and “Other” were classified. Binary time series classifiers were tailored to each of the five behaviors through training and validation of each model across a range of configurations including nine window sizes, five classifiers and a feature selection process of 84 candidate input features. Results revealed that the proposed model classified grazing behavior with an extremely high classification accuracy (F-score of 0.98), whilst ruminating and resting behaviors were also classified with a high accuracy (F-scores of 0.87 and 0.85, respectively), and walking was classified with a lower accuracy (F-score of 0.73). The proposed model offered a 5% performance improvement over standard multi-class time series classifiers that was attributed to the “one-vs-all” framework training a classifier for each behavior independently; this created diversity in the behavior model. The feature selection process used in developing each of the binary classifiers found that features representing the motion intensity and pitch of the cow’s head were most important to each of behavior’s classification. Whilst a minor performance improvement was obtained using the proposed methodology, it is suggested that further performance improvements could be obtained by increasing the diversity of the classifier’s inputs. Diversity could be created by fusing the data of other sensors that can be fitted to the cows i.e. GPS tracking unit, pressure sensor and microphone.

A comparison of autoencoder and statistical features for cattle behaviour classification.

A study is presented comparing the effectiveness of unsupervised feature representations with handcrafted features for cattle behaviour classification. Precision management of cattle requires the interaction of individual animals to be continuously monitored on the farm. Consequently, classifiers are trained to infer the behaviour of the animals using the observations from the sensors that are fitted upon them. Historically, domain knowledge drives the generation of features for cattle behaviour classifiers. When new behaviours are introduced into the system, however, it is often necessary to modify the feature set; this requires additional design and more data. Autoencoders, on the other hand, can skip this design step by learning a common, unsupervised feature representation for training. Whilst stacked autoencoders successfully represent structured data including speech, language and images, deep networks have not been used to model cattle motion. Hence, we investigate using a stacked autoencoder to learn a feature representation for cattle behaviour classification. Experimental results demonstrate that the autoencoder features perform reasonably well in comparison to the statistical features that are selected using prior knowledge of behaviour motion.

Potential of summer-active temperate (C3) perennial forages to mitigate the detrimental effects of supraoptimal temperatures on summer home-grown feed production in south-eastern Australian dairying regions

Abstract. In many south-eastern Australian dairying regions, supraoptimal ambient temperatures (Ta > 30°C) often challenge the perennial ryegrass (Lolium perenne L.)-dominated feed-base during the summer months. A glasshouse experiment was undertaken to identify alternative summer-active temperate (C3) perennial forages more tolerant of supraoptimal temperature stress (day/night Ta of 38/25°C) than perennial ryegrass. Supraoptimal temperature stress was imposed both with and without irrigation. Chicory (Cichorium intybus L.) was the only species to survive 18 days of combined supraoptimal temperature stress and non-irrigation. Lucerne (Medicago sativa L.), plantain (Plantago lanceolata L.), and tall fescue (Festuca arundinacea Schreb.) survived 12 days of this treatment. Twelve days of exposure to these conditions caused death of perennial ryegrass, prairie grass (Bromus catharticus Vahl.), cocksfoot (Dactylis glomerata L.), birdsfoot trefoil (Lotus corniculatus L.), and red clover (Trifolium pratense L.). Irrigation (daily to through drainage) mitigated detrimental effects of imposed supraoptimal temperature stress on the growth and survival of all species. Chicory and to a lesser extent lucerne, plantain, and tall fescue may have a role to play in south-eastern Australian dairying regions, where supraoptimal temperature stress is a frequent and ongoing issue.

Behavior Classification of Dairy Cows Fitted with GPS Collars

Precision management systems for livestock offer the potential to monitor and manage animals on an individual basis. A key component of these sensor based systems are the analytical models that automatically translate sensor data into different behavioral categories.