Dynamic cattle behavioural classification using supervised ensemble classifiers

Dutta, Ritaban; Smith, Daniel; Rawnsley, RP; Bishop-Hurley, Greg; Hills, James; Timms, Greg; Henry, D. A.

doi:10.1016/j.compag.2014.12.002

Cited by 160 publications

(88 citation statements)

References 24 publications

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“…In the previous studies involving three‐axis accelerometers mounted to the head or neck of cows, sampling frequency settings varied: every 1 s (Oudshoorn et al., ; Watanabe et al., ), 0.5 s (Mattachini, Riva, Perazzolo, Naldi, & Provolo, ), 0.1 s (Dutta et al., ; Martiskainen et al., ; Shen et al., ; Werner et al., ), 0.02 s (Diosdado et al., ), and 0.01 s (Scheibe & Gromann, ). For this study, sampling frequency was set at 20 Hz (per 0.05 s) which enabled proper behavioral classification.…”

Section: Discussionmentioning

confidence: 99%

“…() set the time window as 1 min, and Shen et al. () set the time window as 1 s. Different time windows have also used: 2 hr (Borchers et al., ), 10 s (Martiskainen et al., ), 5 s (Dutta et al., ), and 1–10 min (Diosdado et al., ). When changes in behavior patterns are frequent, the time window should be set for a short range, but it results in a huge data set, making analysis more complicated.…”

Section: Discussionmentioning

confidence: 99%

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Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Tamura

Okubo

Deguchi

et al. 2019

Animal Science Journal

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Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three‐axis neck‐mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross‐validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three‐axis accelerometers mounted to a collar.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Tamura

Okubo

Deguchi

et al. 2019

Animal Science Journal

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“…There have been several attempts to evaluate the accuracies of different machine learning methods [7,13,15]. However, due to vastly distinct dynamic movement of different animal species, it is unlikely that there will ever be a universal set template for creating ethograms from accelerometry [16,17].…”

Section: Introductionmentioning

confidence: 99%

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

Ladds

Thompson²,

Kadar

et al. 2017

Anim Biotelemetry

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Background: Semi-automating the analyses of accelerometry data makes it possible to synthesize large data sets. However, when constructing activity budgets from accelerometry data, there are many methods to extract, analyse and report data and results. For instance, machine learning is a robust approach to classifying data. We used a new method, super learning, that combines base learners (different machine learning methods) in an optimal manner to achieve overall improved accuracy. Other facets of super learning include the number of behavioural categories to predict, the number of epochs (sample window size) used to split data for training and testing and the parameters on which to train the models. Results:The super learner accurately classified behaviour categories with higher accuracy and lower variance than comparative models. For all models tested, using four behaviours, in comparison with six, achieved higher rates of accuracy. The number of epochs chosen also affected the accuracy with smaller epochs (7 and 13) performing better than longer epochs (25 and 75). Conclusions:Correct model selection, training and testing are imperative to creating reliable and valid classification models. To do so means model fitting must use a wide array of selection criteria. We evaluated a number of these including model, number of behaviours to classify and epoch length and then used a parameter grid search to implement the models. We found that all criteria tested contributed to the models' overall accuracies. Fewer behaviour categories and shorter epoch length improved the performance of all models tested. The super learner classified behaviours with higher accuracy and lower variance than other models tested. However, when using this model, users need to consider the additional human and computational time required for implementation. Machine learning is a powerful method for classifying the behaviour of animals from accelerometers. Care and consideration of the modelling parameters evaluated in this study are essential when using this type of statistical analysis.

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“…However, the transmission demands high bandwidth which dramatically reduces the precious battery life of a collar tag due to the high energy consumption of radios. Recent studies acknowledge the potential of collaring applications and have evaluated offline activity recognition of cows [7,9,14,25,48], sheep [24,46], and vultures [31]. Smith et al [44] studied features in cattle behavior models, using a greedy search to identify feature subsets that were most effective in classifying activities of steers.…”

Section: Related Workmentioning

confidence: 99%

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Kamminga

Meijers

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust to the sensor's orientation. Our approach comprises four main steps. First, 3D feature vectors are selected since they are theoretically independent of orientation. Second, the least interesting features are suppressed to speed up computation and increase robustness against overfitting. Third, the features are further selected through an embedded method, which selects features through simultaneous feature selection and classification. Finally, feature sets are optimized through 10-fold cross-validation. We collected real-world data through multiple sensors around the neck of five goats. The results show that activities can be accurately recognized using only accelerometer data and a few lightweight features. Additionally, we show that the performance is robust to sensor orientation and position. A simple Naive Bayes classifier using only a single feature achieved an accuracy of 94 % with our empirical dataset. Moreover, our optimal feature set yielded an average of 94 % accuracy when applied with six other classifiers. This work supports embedded, real-time, energy-efficient, and robust activity recognition for animals.

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Dynamic cattle behavioural classification using supervised ensemble classifiers

Cited by 160 publications

References 24 publications

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

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