Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches

McVey, Catherine; Hsieh, Fushing; Manriquez, Diego; Pinedo, Pablo; Horback, Kristina

doi:10.3390/s22010001

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Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches

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Department of Animal Science, University of California Davis, Davis, CA 95616, USA

Department of Statistics, University of California Davis, Davis, CA 95616, USA

Department of Animal Science, Colorado State University, Fort Collins, CO 80523, USA

Author to whom correspondence should be addressed.

Academic Editor: Yongliang Qiao

Sensors 2022, 22(1), 1; https://doi.org/10.3390/s22010001

Received: 30 October 2021 / Revised: 11 December 2021 / Accepted: 17 December 2021 / Published: 21 December 2021

(This article belongs to the Collection Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming)

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Abstract

Large and densely sampled sensor datasets can contain a range of complex stochastic structures that are difficult to accommodate in conventional linear models. This can confound attempts to build a more complete picture of an animal’s behavior by aggregating information across multiple asynchronous sensor platforms. The Livestock Informatics Toolkit (LIT) has been developed in R to better facilitate knowledge discovery of complex behavioral patterns across Precision Livestock Farming (PLF) data streams using novel unsupervised machine learning and information theoretic approaches. The utility of this analytical pipeline is demonstrated using data from a 6-month feed trial conducted on a closed herd of 185 mix-parity organic dairy cows. Insights into the tradeoffs between behaviors in time budgets acquired from ear tag accelerometer records were improved by augmenting conventional hierarchical clustering techniques with a novel simulation-based approach designed to mimic the complex error structures of sensor data. These simulations were then repurposed to compress the information in this data stream into robust empirically-determined encodings using a novel pruning algorithm. Nonparametric and semiparametric tests using mutual and pointwise information subsequently revealed complex nonlinear associations between encodings of overall time budgets and the order that cows entered the parlor to be milked. View Full-Text

Keywords: dairy welfare; hierarchical clustering; mutual information; precision livestock farming; time budgets; unsupervised machine learning

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McVey, C.; Hsieh, F.; Manriquez, D.; Pinedo, P.; Horback, K. Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches. Sensors 2022, 22, 1. https://doi.org/10.3390/s22010001

AMA Style

McVey C, Hsieh F, Manriquez D, Pinedo P, Horback K. Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches. Sensors. 2022; 22(1):1. https://doi.org/10.3390/s22010001

Chicago/Turabian Style

McVey, Catherine, Fushing Hsieh, Diego Manriquez, Pablo Pinedo, and Kristina Horback. 2022. "Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches" Sensors 22, no. 1: 1. https://doi.org/10.3390/s22010001

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