Together with Weihenstephan University of Applied Sciences – TUM Campus Straubing, Computomics is working on “New machine learning techniques for more accurate plant breeding by integrating heterogeneous external factors“ funded by the BMBF.
The agricultural industry is under great pressure to deliver new crop varieties quickly for a changing climate and to use fewer resources. The goal is to increase yield and become more sustainable. To accelerate breeding programs, plant breeders are using genomic selection methods to predict the expected value of a trait such as yield from the genetic profiles of plants before the plants have been tested in the field. The trait expression of plants is influenced by two main factors: their genetic, i.e. inherited, traits and the environment in which they grow.
The aim of the joint project “CropML” is to develop machine learning (ML) models that take both into account, i.e. environmental influences in addition to genetics. To this end, data describing the environment will be integrated, e.g. measured values of weather, soil conditions or agronomic factors such as fertilizer use. During the project, suitable data sources for environmental descriptions will be identified and pre-processed to be compatible with genetic data for ML models. New ML methods will be developed that can integrate the very heterogeneous data from genetic profiles and environmental factors and model the influence of both sources on the trait to be predicted, especially their interaction.
The methods developed will be largely automated to provide breeders with rapid information for time-critical decisions. This will allow more precise selection of promising varieties. It will also help identify suitable varieties for new regions and changing climates. By using the developed methods, breeders will gain an economic and ecological advantage by breeding better and more robust varieties with fewer resources.
Source : seedworld