New approaches are necessary to meet the goals of increased food production. Plant breeding can play a key role by developing cultivars with higher yield potential. New technologies like genomic selection and high-throughput phenotyping offer possibilities to increase genetic gains through more precise selection and shortening of the breeding cycle. However, considerable research is needed in terms of theoretical developments, statistical modeling and technical solutions to achieve this in practice. By bringing in world-leading expertise in statistical modeling and image analysis, we will develop novel statistical models to extract biologically relevant information from hyperspectral images. The work will consist of developing reliable methods for capturing high-resolution images of field plots, and utilizing novel computational solutions to integrate top view images from drones with close-up images from robots to build 3D models that retain the original resolution and hyperspectral information. Computational algorithms will then be used to extract important physical and physiological traits from these 3D models that can be used directly as selection tools in plant breeding. By coupling hyperspectral data with grain yield and other direct measurements, statistical prediction models will be developed that plant breeders can use in early-generation selection to increase yield gains. Considerable efforts will be spent on developing efficient computational solutions to manage the large amounts of data that will be generated, and finding intuitive ways of displaying relevant information to the plant breeder. User-friendly solutions will be developed through direct involvement of Graminor plant breeders in the project. By utilizing virtual reality technology, our ultimate goal is to "take the field to the breeder" and let the plant breeder observe the field plots and associated data through VR goggles.
Project leader: Morten Lillemo
Institution: Institutt for plantevitenskap