Browsing by Subject "Phenotyping"

Now showing 1 - 5 of 5
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    Characterizing the impact of genetic and environmental variation on maize utilizing phenomic approaches
    (2020-08) Tirado, Sara
    To gain a better understanding of how genotypic elements interact with the environment, we need to develop more efficient ways of monitoring plant phenotypes so that we can gather phenotypic data through time. Remote sensing technologies provide effective means to do this. Sensor, computational, and platform technologies keep evolving and provide avenues to evaluate how plants grow and respond to environmental conditions. The goals of this thesis were to develop methods and analytical procedures to evaluate maize plant phenotypic performance under varied environmental conditions. The first set of experiments were geared towards utilizing unmanned aerial vehicles equipped with RGB cameras to assess how maize plants grow in response to various field management conditions, including various planting dates and densities, and weather events. These tools were used to assess variation for lodging responses and downstream impacts on productivity. More generally, results from this thesis demonstrate that measurements collected early in development can be useful for improving predictions of end-season traits. The second set of experiments provided insights into using hyperspectral technologies for genotypic differentiation and abiotic stress detection. There is a large amount of variation in reflectance throughout maize leaves that can be useful in distinguishing different maize genotypes at the seedling stage and for detecting and quantifying abiotic stress conditions including cold, heat, and salt stress early in development. By documenting phenotypic differences across genotypes and environments through time in a more efficient manner by taking advantage of available remote sensing technologies, we can improve our understanding of how different environmental and genetic elements impact plant productivity and facilitate advancements in crop improvement and production.
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    Deciphering lodging resistance in oat and other cereal crops
    (2019-08) Susko, Alexander
    Lodging impedes the successful cultivation of oat and other cereal crops in the upper midwestern United States. Lodged cereals not only possess reduced grain yields, but also decreased grain quality. This dissertation first conceives of a camera system to capture plant movement in the wind in the field via a 360-degree field of view camera, followed by a video analysis pipeline to quantify the frequency and amplitude of cereal stem movement under varying wind conditions in the field. The natural oscillating frequencies and amplitudes of stems were dependent on wind speeds and at the cultivar, crop level. Nonetheless, the substantial environmental effects in the field that induce lodging make discovering specific morphologies that confer lodging resistance difficult. Next, in seeking to better identify promising morphological targets for breeding and selecting lodging resistance in cereal crops, a diverse panel of 38 cereal cultivars (oat, wheat, barley) were subjected to replicated testing in a wind tunnel. Wind tunnel testing revealed that a cereal ideotype consisting of low total biomass, high stem strength, and high stem elasticity should confer increased lodging resistance. A field study using the camera system to quantify aspects of plant movement and correlated these phenotypes with physical plant traits is presented next, which indicated that patterns of plant movement are spatially independent in a randomized complete block design of 16 cereal cultivars and that the relationships between plant height, heading date, and plant movement vary among the major cereal crops. Finally, a GWAS and QTL validation study is presented on lodging in oat, which revealed significant marker trait associations for plant height, heading date, and stem snapping, though only QTL for plant height and heading date were successfully validated in derived biparental populations.
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    Hyperspectral image dataset for salt stress phenotyping of wheat
    (2018-04-13) Moghimi, Ali; Yang, Ce; moghi005@umn.edu; Moghimi, Ali; Moghimi, Ali
    The dataset contains hyperspectral images of four wheat lines, each with a control and a salt (NaCl) treatment. Images were captured by a hyperspectral camera (PIKA II, Resonon) under natural light condition one day after salt application when there were no visual symptoms in wheat plants. The camera recorded the spectral response of both control and salt tanks of each line over 240 spectral channels in visible and near infrared region (400 nm to 900 nm) with about 2.1 nm spectral resolution, 640 spatial channels in the cross-track direction, and about 1 mm spatial resolution. Raw images were converted to radiance (Wm−2sr−1nm−1) using a vendor-provided calibration file, and then converted to reflectance (%) using a Spectralon panel. In total 25 spectral bands were disregarded due to high noise. Subsequent to noisy band removal, vegetation pixels were segmented from background using spectral vegetation indices and morphological operation. Although the goal of this study was plant phenotyping to rank salt tolerance of wheat lines, this dataset can be used for other research purposes, such as developing classification algorithms to discriminate healthy and stressed plants and developing methods for spectral feature selection to reduce the dimension of hyperspectral images.
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    UAV-based hyperspectral dataset for high-throughput yield phenotyping in wheat
    (2020-01-14) Moghimi, Ali; Yang, Ce; Anderson, James A.; moghi005@umn.edu; Moghimi, Ali; University of Minnesota
    The dataset was collected by a hyperspectral camera (PIKA II, Resonon, Inc.) mounted on an unmanned aerial vehicle (UAV, DJI Matrice 600 Pro) from three experimental yield trial fields (C3, C4, and C9) during two consecutive growing seasons 2017 (C3 and C9) and 2018 (C4). The aerial hyperspectral images were captured within two weeks prior to harvest over 240 spectral channels in visible and near infrared region (400 nm to 900 nm) with about 2.1 nm spectral resolution and about 2 cm spatial resolution. Subsequent to radiometric calibration and noisy band removal, plots were cropped from the hyperspectral images and saved as 3D matrices with Matlab (MAT files) and Python (NPY files) format. The dataset entails hyperspectral cubes of 1021 wheat plots and the grain yield of plots harvested by a combine. The corresponding ground truth data (yield) for each hyperspectral cube representing a plot can be found based on the field (e.g., C3, C4, and C9) and plot ID.
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    Using High-Throughput Phenotyping To Investigate The Genetic Bases Of Quantitative Traits In Hybrid Wine Grape (Vitis Spp.)
    (2019-04) Underhill, Anna
    High-throughput phenotyping methods have gained popularity in the plant sciences due to their potential to more quickly collect data, reduce human error, and investigate plant characteristics in new ways. In grapes, many economically important traits are quantitative, varying across a spectrum and displaying diverse phenotypes. Though rating scales exist for such traits, their usefulness can be limited by their ability to capture variation across populations; additionally, they require judgement that can vary based on the individual scoring the trait. Automated systems can be used to remedy these issues, eliminating subjectivity and more fully describing phenotypic variation. In these experiments, a semi-automated image analysis system was used to evaluate fruit cluster compactness and berry color in a multispecies hybrid wine grape (Vitis spp.) population. First, color-based image segmentation was used to isolate components of the fruit cluster morphology. Berry color was quantified using several different color spaces, and a MATLAB program was written to measure several morphological components to evaluate cluster compactness. Both color and compactness traits were used to perform quantitative trait loci (QTL) mapping, where associations between the traits and genetic regions were identified. Known QTL for berry color on chromosome 2 were identified, along with several minor QTL associated with color and anthocyanin content. Image-derived traits were associated with known QTL such as the chromosome 9 rachis length QTL, and also identified other regions of interest relating to cluster compactness. Altogether, these projects demonstrate the advantages of high-throughput phenotyping methods and their ability to identify new variation among quantitative traits.