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. 2018 Apr 14;96(4):1540-1550.
doi: 10.1093/jas/sky014.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture

Gota Morota  1 , Ricardo V Ventura  2   3 , Fabyano F Silva  4 , Masanori Koyama  5 , Samodha C Fernando  1
Affiliations

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture

Gota Morota et al. J Anim Sci. .

Abstract

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.

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Figures

Figure 1.
Figure 1.
Overview of the cross-validation framework.
Figure 2.
Figure 2.
Summary of terminologies, including models, regularization, supervised learning, and unsupervised learning.
Figure 3.
Figure 3.
Overview of big data analysis in animal science using machine learning and data mining tools.
See this image and copyright information in PMC

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