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Detekce napadení ječmene listovými chorobami pomocí infračerveného termálního zobrazování, zobazovací fluorescence chlorofylu, UV-stínění fluorescence chlorofylu a hyperspekrálního zobrazování
Holzmannová, Kateřina
The aim of the thesis was, in the theoretical part, to summarize basic information about barley, its use and the leaf diseases that occur most often on barley, and also to summarize the basics of imaging methods potentially useful for the detection of diseases on plant leaves, namely chlorophyll fluorescence imaging, chlorophyll fluorescence UV shielding , hyperspectral imaging and infrared thermal imaging. The aim of the work was to evaluate the potential of individual sensor methods from the point of view of their ability to detect the resistance of spring barley genotypes to powdery mildew based on the experiment carried out with the inoculation of spring barley with grass powdery mildew (Blumeria graminis f. sp. hordei). In the practical part, selected sensor methods are compared within the experiment with 6 lines of spring barley (line of the Pallas variety) differing only in the presence of different genes for resistance to powdery mildew. These lines were chosen to represent a wide range of reaction types in response to infection with avirulent powdery mildew pathotypes, as reaction type can be reflected in the response measured using these indirect methods. Based on the correlation analysis performed for individual parameters measured using tested indirect sensor methods with the values of the light-saturated CO2 assimilation rate, it was possible to state that within each group of methods there is a parameter showing a high ability to predict the plant's response and thus resistance to powdery mildew. It is a chlorophyll index, based on the principle of measuring the infrared and red transmittance of the leaf, the difference between the leaf temperature and the air temperature determined using a thermal camera, the fluorescence decrease ratio - vitality index (Rfd), and finally also the ZM spectral index. In addition to the temperature difference, the values of the Pearson correlation coefficient reach the level of 0.74, which means that it is a highly evident relationship, and the adjusted R2 values are above 0.55, which means that the reliability of the prediction is higher than 55%.
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