Data modelling for animal phenomics

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One of the major limiting factors impacting multiple areas of animal science such as livestock breeding, production and genomics is related to collecting, managing and effectively modeling abundant, real-time, high-throughput and high-quality phenotypic data. Significant advancements in closing the gap between phenotypes and genotypes and developing a better understanding of complex interactions between phenotypes and production systems can be achieved by standardizing and automating phenotype collection protocols, improving the underlining collection technologies and augmenting the collected phenotypic information with environmental, geographic, genotypic, physical and physiological data 1,2. Significant efforts are being made by both, researchers and private businesses, to develop systems and technologies that harvest, store, model and analyze animal-related information synchronously within periodical and pre-established time frames relevant to specific phenotypic features and various steps of multiple production value chains. Given the large number of components of an animal phenotype such as morphological, developmental, biochemical, physiological and behavioural, this research proposal will focus on the study of phenotypes related to animal growth. The short-term goals of my research project include: (S1) developing efficient and accurate semi-automatic and automatic image acquisition and processing protocols for pigs, dairy and beef cattle using affordable sensors and reference objects, (S2) extracting and analyzing morphometric information (e.g. body dimensions, posture, body condition score and motion patterns) from images using data mining, computer vision and machine/deep learning methods, (S3) investigating the level of correlation among various morphometric measurements, animal physiological states and animal development/growth and their putative use in models capable to estimate or predict growth, and (S4) developing species-specific regression and classification phenotypic models for estimation of growth represented by live body weights and body condition scores (and other relevant existing and potentially new traits) based on digital images that include reference objects with known dimensions. The long-term goals include: (L1) researching methods that increase the accuracy and usability of semi-automatic and automatic phenotypic data collection, which can be further integrated with complementary livestock information to produce animals more efficiently and more sustainably, and to improve economic outcomes on the farm, (L2) collecting and integrating phenotypic results into models that can be used to develop predictive decision support systems for the Canadian industry, and (L3) recruiting and training highly qualified personnel in livestock phenomics.