We are deploying traditional and high-throughput phenotyping (HTP) tools and platforms including manned ground and unmanned aerial systems to improve yield. We integrate HTP with genome-wide association and prediction to advance the genetic gain. Some of our research interests include the study of yield response in diverse environments and yield prediction, physiogenetics of yield, plant framework to assemble higher yield, the use of big data analytics to predict yield, and the application of machine learning (ML) algorithms for trait phenotyping including yield. We look to achieve an increased seed yield per unit of land with similar or reduced input costs to allow a more profitable and sustainable production. We use elite and exotic germplasm sources to improve yield and develop cultivars.
Components and research tools include: traditional and high-throughput phenotyping through manned and aerial systems, digital imaging, predictive phenomics, machine learning applications, genome-wide association, genome-wide prediction and selection, and genotyping technologies.
People: Brungardt, Jae L; Carroll, Matthew E; Falk, Kevin; Higgins, Race H; Hicks, Jennifer R; Parmley, Kyle A; Scott, Brian W; Zhang, Jiaoping