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.
Project leader: Øyvind Nordbø
Institution: GENO SA