Doctor of Philosophy / Genetics Interdepartmental Program / Jesse Poland / The Green Revolution is credited with saving billions of lives by effectively harnessing new genetic resources and breeding strategies to create high-yielding varieties for countries lacking adequate food security. To keep the next billion people in a state of food security, plant breeders will need to rapidly incorporate novel approaches and technologies into their breeding programs. The work presented here describes new genomic and phenomic strategies and tools aimed at accelerating genetic gain in plant breeding. Plant breeders have long relied on regional testing networks to evaluate new breeding lines across many locations. These are an attractive resource for both retrospective and contemporary analysis due to the vast amount of data available. To characterize genetic progress of plant breeding programs in the Central Plains, entries from the Southern Regional Performance Nursery dating back to 1992 were evaluated in field trials. The trend for annual improvement was 1.1% yr⁻¹, matching similar reports for genetic gain. During the same time period, growth of on-farm yields stagnated. Genomic selection, a promising method to increase genetic gain, was tested using historical data from the SRPN. A temporal-based model showed that, on average, yield predictions outperformed a year-to-year phenotypic correlation. A program-based model found that the predictability of a breeding program was similar when using either data from a single program or from the entire regional collection. Modern DNA marker platforms either characterize a small number of loci or profile an entire genome. Spiked genotyping-by-sequencing (sGBS) was developed to address the need in breeding programs for both targeted loci and whole-genome selection. sGBS uses a low-cost, integrated approach that combines targeted amplicons with reduced representation genotyping-by-sequencing. This approach was validated using converted and newly-designed markers targeting known polymorphisms in the leaf rust resistance gene Lr34. Plant breeding programs generate vast quantities of data during evaluation and selection of superior genotypes. Many programs still rely on manual, error-prone methods to collect data. To make this process more robust, we have developed several open-source phenotyping apps with simple, intuitive interfaces. A contemporary Green Revolution will rely on integrating many of these innovative technologies into modern breeding programs.

Master of Science / Department of Computing and Information Sciences / Mitchell L. Neilsen / This research is a part of BREAD PHENO, a PhenoApps BREAD project at K-State which combines contemporary advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. In this platform, novel image analysis segmentation algorithms are being developed to model and extract plant phenotypes. As a part of this research, the traditional Watershed segmentation algorithm has been extended and the primary goal is to accurately count and characterize the seeds in an image. The new approach can be used to characterize a wide variety of crops. Further, this algorithm is migrated into Android making use of the Android APIs and the first ever user-friendly Android application implementing the extended Watershed algorithm has been developed for Mobile field-based high-throughput phenotyping (HTP).