Development of Image Analysis Tools to Quantify Potato Tuber Quality Traits

Abstract

Potato is the most popular non-cereal food crop and a major staple crop. Despite the importance of potato, it has seen little yield improvement through breeding over the past century when compared to other crops. One difficulty in potato breeding is the large number of quality traits that must be accounted for in order to create marketable potato varieties. These quality traits are often measured using imprecise, subjective scales. This thesis covers my work in improving the tools available for use in measuring and breeding for potato tuber quality traits. In Chapter 1, I review the literature relevant to a selection of quality traits and their measurement. I discuss machine learning and its use in identifying more intricate tuber quality traits, as well as efforts to perform genomic selection in autotetraploid potato as a possible application for highly quantitative quality trait data. Chapter 2 covers the mechanics and capabilities of the potato tuber image analysis program, TubAR. I compare the quantitative measurements provided by TubAR to human visual scores for analogous traits. In Chapter 3, I discuss efforts to expand the scope of traits able to be measured with image analysis by employing machine learning image classification, using the pressure bruise and skin finish traits.