Reliable and efficient high-throughput phenotyping to accelerate genetic gains in Norwegian plant breeding (virtual phenomics; vPheno)

Reliable and efficient high-throughput phenotyping to accelerate genetic gains in Norwegian plant breeding (virtual phenomics; vPheno)

vPheno project activities

Use of drones and robots to accelerate genetic gains in Norwegian wheat breeding

prosjekt

About/Aims
Background

New plant varieties are needed for increasing crop yields and adapting agriculture to climate change. This project is developing and testing new technologies in both image analysis and genomic prediction that will facilitate a more precise selection of new plant varieties. Drones and a specially designed agricultural robot are fitted with multispectral cameras and used to automatize the gathering of data from field trials.

Objective

Main objective: To enable faster genetic gains in Norwegian cereal breeding through more precise phenotyping coupled with use of genomics data.

This will be achieved through the following secondary objectives:

  1. Establishment of reliable platforms for field phenotyping tailored to Norwegian conditions
  2. Development of efficient statistical frameworks for data analysis and prediction of commercially important traits based on high-throughput phenotyping data together with pedigree and genomic information.
  3. Integration of developed tools and methods into reliable and efficient user-friendly systems.
More about the project

Tools developed in this project will give the plant breeder access to more precise data on the growth and development of the plants. In parallel, we are investigating genomic prediction models for Norwegian wheat breeding and ways to further improve prediction models by incorporating multispectral imaging data. Finally, we develop new ways to visualize field trial data through virtual reality by combining the three-dimensional models from the multispectral image analyses with other relevant information from the field trial plots.

The project is organized in five work packages:

WP0: Project management (WP leader: Morten Lillemo, NMBU)
WP1: Development of reliable platforms for high-throughput data capture (WP leader: Ingunn Burud, NMBU)
WP2: Building 3D models of field trials and modelling pixel to phenotype relationships (WP leader: Jose Crossa, CIMMYT, Mexico)
WP3: Adapting and integrating tools and methods into reliable and user-friendly systems (WP leader: Muath Alsheikh, Graminor)
WP4: Communication and knowledge dissemination (WP leader: Kristin Børresen, Graminor)