BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1

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.

PigLeg: prediction of swine phenotype using machine learning

“…Rapidly developing data mining approaches are of increasing interest because they provide for acquisition and analysis of information that results in predictive productivity indicators for animals (Morota et al, 2018;Putz et al, 2018;Howard, 2019). Machine learning (ML) approaches have been successfully used in animal husbandry for early prediction of the growth and quality of adult wool in Australian merino sheep (Shahinfar & Kahn, 2018), sheep carcass traits from early-life records (Shahinfar, Kelman & Kahn, 2019), and skin temperature of piglets (Gorczyca et al, 2018).…”

ClassiPhages 2.0: Sequence-based classification of phages using Artificial Neural Networks

“…Finally, we expect the population of phage families with low abundant phages, from viral metagenomic datasets analysis. Since ANNs are known to perform better with an increasing size of a benchmark dataset (Morota et al 2018), we foresee the improvement of ClassiPhage 2.0.…”

Variations in the body surface temperature of sows during the post weaning period and its relation to subsequent reproductive performance

“…Recent developments in artificial intelligence technologies have enabled them to continu ously monitor biological and environmental information using less laborintensive approaches for animal production systems. In particular, the use of fully automated data recording or phenotyping platforms based on digital images, sensors, sounds, unmanned systems, and realtime noninvasive computer vision are gaining momentum and have great potential to enhance product quality, management practice, welfare, sustainable development, and animal health [1,2]. The new concept of Precision Livestock Farming may establish the use of such systems by making them easier for management and facilitating prevention of poor welfare by identifying early stages of disease and stressful situations.…”

“…1) DPW, day post-weaning on which heat was first shown; NDPW, day post-weaning on which not detected in standing heat. 2) Number of sows observed standing heat. 3) Number of sows in AM and PM subgroups (AM:PM).…”

“…1) Number of sows observed with successful parturition. 2) Number of successful parturition sows in AM and PM subgroups (AM:PM).…”