International Wheat Yield Partnership
Wheat yield prediction and advanced selection methodologies through field-based high-throughput phenotyping with UAVs
To realize a new level of yield potential, breeding programs must increase the speed of developing new varieties (increase the rate of genetic gain) by evaluating larger populations, making more accurate selections, and decreasing the length of the breeding cycle. Genomic advancements during the past decade have enabled genomic prediction and selection of complex traits on larger number of breeding lines and at early stages in the breeding cycle. At the same time, however, phenotyping of breeding lines under field conditions has seen minimal advancement and is a critical bottleneck for evaluating large populations.
Through this project, we are applying novel developments in remote sensing with unmanned aerial vehicles (UAVs) analyzed with machine vision and deep learning to make improved yield predictions directly within breeding programs in the US and internationally at the International Maize and Wheat Research Center (CIMMYT). We have tested and optimized UAV platforms that can be deployed in field crop breeding programs. Using the rich image datasets from the UAVs, we are working to generated in-season yield predictions by combining genomic information with multiple levels of proximal sensing and deep learning on tens of thousands of breeding lines. These optimized selection strategies using the full array of genomic and phenotypic information will be assessed and delivered to breeders.
To disseminate these advancements beyond the immediate project team across the breeding community, we are developing user-friendly software tools to analyze UAV imagery, along with efficient algorithms to generate breeder-ready predictions for selection.
This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2017-67007-25933