Phenotyping 4 Profit – for disease resistance in plants
To accelerate resistant variety development in plant breeding programs precise, non-destructive and quantitative methods to evaluate plant disease resistance or susceptibility are crucial. In this project we will select use cases for experiments in growth chambers, greenhouses and open fields, with the aim to increase the throughput of data acquisition, further develop quantification and pattern recognition using artificial intelligence both in time and in accuracy.
This project is strategically linked to key technology phenotyping, focused on the development of accurate testing and measuring methods for new and existing traits to support breeding, preferably high throughput and combined with big data. Within the project we will develop procedures for new and more accurate characterization of disease and disease resistance. We will record images and meta-data using high through-put systems and the massive amount of rich annotated data will be captured, stored and analyzed using big-data procedures, including artificial intelligence techniques to link the recorded data to the assessment done by experts. The results from the large scale experiments will be adapted and optimized within this project to fit the specific requirements of plant breeding researchers and industrial partners involved.
Breeding for resistance is a practically proven strategy to reduce chemical input providing real life solutions in agriculture today. The research and experiments described in this project will advance the transition to novel strategies of phenotyping and data driven research and will enable plant breeding for resistance to keep pace with the growing societal needs for food security and food quality.
This project will enhance competitiveness of the industrial partners that will bring these solutions to the market.
This project will advance technology in the field of phenotyping. It will generate protocols for efficient high resolution (HR) and HTP phenotyping for resistance to pathogens and pests. This has both economic as ecological implications. Pathogens and pest cause huge losses ranging from 17 % to 30 % in many crops threating our goals set for food security and sustainable agriculture. Control strategies frequently depend on the use of agrochemicals with possible negative ecological implications. Plant resistance offers a more sustainable and cost effective alternative in management strategies. However, breeding for resistance is not an easy task and innovations in the field of phenotyping are urgently needed. The desired final outcomes of the project links directly to societal needs for a more robust production systems and reduced chemical input. The projects facilitates breeding practices by reducing cost decreasing lead time and increase confidence. Data driven techniques including large scale phenotyping are a logical option. Increased understanding of resistance and availability of cultivars resistant to pathogens and pest will be beneficial in any IMP based strategy.
Yearly progress will be discussed in Q3 and at which a proposal will be presented for the research in the next year. Based on the input from all consortium partners in an annual meeting in Q1 the schedule for the upcoming year is finalized for the upcoming year