Database Research Summaries2018 Using engineering tools to identify and quantify biotic and abiotic stress in soybean for customizable agriculture production
The focus of the project is to meet the future demand of food, feed, fiber, and fuel, crop production needs to be doubled by 2050.
- Use hyperspectral camera to develop disease signatures to distinguish among SCN, SDS, BSR, Charcoal rot and IDC.
- Develop algorithms to differentiate diseases with confounding symptoms
- Differentiate between SCN infestation and IDC in field
- Differentiate between SDS and BSR (only feature to distinguish them currently is to cut stems)
- Develop algorithm to count SCN eggs under the microscope in a rapid and accurate manner.
Identify useful sources of resistance to SCN using manual and image based phenotyping (using egg detection algorithm developed in this project). This objective will leverage the SCN egg detection algorithm developed in year 1, (part of objective 3(a)) for high throughput egg detection and counting.
- Conduct genomic wide association study to identify useful genes controlling SCN resistance, and to deploy genomic selection approaches to develop selection strategies for SCN resistance.
Results will be added once the project is finished.
- Farmers can scout field for diseases using their phone.
- Farmers will save input costs and create a healthier and safer environment.
- Farmers will increase their profits due to reduced chemical costs.
For more information about this research project, please visit the National Soybean Checkoff Research Database.
Funded in part by the soybean checkoff.