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NAERINGSPH-Nærings-phd

3D shape analysis of cattle to improve data collection

Alternative title: 3D formanalyse av storfe for forbedret data-fangst

Awarded: NOK 2.3 mill.

To improve genetic gain in cattle breeding, Geno SA and TYR wants to improve the data collection. A lot of manual work is currently needed to record some traits, and we want to want to automatize this data collection by utilizing 3D imaging technology. To extract traits for breeding from 3D images, we need to develop algorithms, predicting meaningful quantitative measures about the animal's size shape or quality, based on 3D images. In this project, we want to utilize machine learning techniques to predict traditional traits, like the bull's weight or the teat positioning of a cow. We have built data sets consisting of both 3D images and the traditional measures we want to improve. On these data sets, we are using supervised learning to train the deep neural networks to predict the traditional traits based on the 3D images. Given a relevant 3D image, the trained model should be able to predict the traditional trait, and the traditional recording could then be skipped, which could both improve and streamline data collection on cattle. Until now, we have been developing deep learning based classifiers to distinguish between useful and non-useful images. Since some blurry or irrelevant test-images are taken during the acqusition phase, such images need to be automatically filtered out before further post-processing. Further, we have been working on prediction of supermummerary teats as well as teat position, base don the images.

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To improve genetic gain within cattle breeding, relevant data need to be collected on a large number of animals. However, for some traits, data collection requires a lot of manual work, and automation of these processes has the potential to both save costs and improve the genetic gain. Examples of such traits are weight measurements on beef cattle (where the cattle needs to be located on the weight) and udder conformation on dairy cows, which is currently scored by visual inspection by technicians. Geno, TYR and other collaborators are now in another project testing out whether use of 3D cameras in combination with computer vision could replace traditional recording systems. Main objective: In this project, we want to develop automatic computer vision algorithms that utilize the surfaces to predict geometric properties relevant for the cattle breeding. Reference data sets are under constrcion, where both 3D images and traditional recordings are taken from the same animals. Based on computer vision and machine learning techniques (like e.g. supervised deep learning), we aim to train regression models that could be used to predict relevant phenotypes, based on 3D images. • Secondary objective1: Develop models to predict traditional udder conformation traits, based on 3D images. Traditional conformation score data and predicted traits from 3D images should be used into genetic analyses for comparison and estimation of e.g. heritability. • Secondary objective 2: Develop regression models that are able to predict weight, carcass-quality and body condition score from 3D images of cattle, taken from above. • Secondary objective 3: Develop new relevant traits for breeding. To do this, we aim to build a reference atlas of the udder surface to achieve point correspondence between individuals. Further, by using dimension techniques (like Partial least squares regression), we want to combine the 3D surfaces with health data, to find health risk indicators in complex 3D data.

Funding scheme:

NAERINGSPH-Nærings-phd