Browsing by Subject "image analysis"

Now showing 1 - 3 of 3
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    Development of Image Analysis Tools to Quantify Potato Tuber Quality Traits
    (2022-08) Miller, Michael
    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.
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    High-resolution grape cluster images and color-based segmentations for population GE1025 in 2017 and 2018
    (2019-04-18) Underhill, Anna N; underhillanna@gmail.com; Underhill, Anna N
    This is a collection of images collected in 2017 and 2018 from the UMN grape research population GE1025 (parentage described in Teh et al., 2017) that was used for two image analysis-based phenotyping experiments: one involving cluster compactness, and one involving skin color. Contained in each year are RAW images, color-corrected images, and segmented images for berry, stem, and background. Details about methods used to capture and segment images are available at https://github.com/underhillanna/GrapeImageAnalysis, or in Underhill, 2019.
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    Using R based image analysis to quantify rust on perennial ryegrass
    (2018-11) Heineck, Garett; Watkins, Eric; Jungers, Jacob; McNish, Ian
    Crown and stem rust caused by Puccinia coronata f. sp. lolii and Puccinia graminis subsp. graminicola are major diseases of perennial ryegrass (Lolium perenne L.) when grown for turfgrass, forage, and seed. Plant breeders and pathologists often quantify rust severity in the field using the modified Cobb scale, but this method is subjective, labor intensive, and dependent on the skill and experience of the scorer. Our objective was to develop a novel, open-source system that couples both ImageJ and R to quantify rust severity on simple RGB images.