• Korean Journal of Soil Science and Fertilizer
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• v.20 no.3
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• pp.251-259
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• 1987

Humic acids extracted from decomposing plant residues were characterized by infrared(IR) spectra. The IR spectra were further interpreted by chemical analyses for oxygen-containing functional groups such as carboxyl, phenolic, alcoholic, carbonyl, and quinionic groups. 1. The IR spectra obtained in this study were divied into three categories: spectra of humic acids from grain crop straws of rice, barley, wheat and rye produced Type I, while that from wild grass hay yielded Type II, and those from forest tree litter of the deciduous and conifers were led to give Type III. 2. There were no significant changes in the absorption bands observed among humic acids extracted at various stages of decomposition of a given Plant material. 3. The absorption band at about $3,430cm^{-1}$ represents the presence of hydrogen-bonded hydroxyl groups, phenolic-OH groups being the major component. 4. A close relationship was found between the total acidity and the content of phenolic-OH groups of humic acids. The content of carboxyl groups maintains a direct relationship with the content of total hydroxyl groups, and such a close relationship also exists between the content of alcoholic hydroxyls and that of total hydroxyl groups. 5. Overlapping of the absorption bands of carbonyl groups and quinones renders it difficult to make differentiation between the two. 6. A variety of non-armoatic cyclic hydrocarbons appears to be a structural component as evidenced by a sharp absorption peak near $995-1000cm^{-1}$.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.15-33
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• 2021

In this study, the possibility of discriminating Fire blight (FB) infection tested using the hyperspectral imagery. The reflectance of healthy and infected leaves and branches was acquired with 5 nm of full width at high maximum (FWHM) and then it was standardized to 10 nm, 25 nm, 50 nm, and 80 nm of FWHM. The standardized samples were divided into training and test sets at ratios of 7:3, 5:5 and 3:7 to find the optimal bands of FWHM by the decision tree analysis. Classification accuracy was evaluated using overall accuracy (OA) and kappa coefficient (KC). The hyperspectral reflectance of infected leaves and branches was significantly lower than those of healthy green, red-edge (RE) and near infrared (NIR) regions. The bands selected for the first node were generally 750 and 800 nm; these were used to identify the infection of leaves and branches, respectively. The accuracy of the classifier was higher in the 7:3 ratio. Four bands with 50 nm of FWHM (450, 650, 750, and 950 nm) might be reasonable because the difference in the recalculated accuracy between 8 bands with 10 nm of FWHM (440, 580, 640, 660, 680, 710, 730, and 740 nm) and 4 bands was only 1.8% for OA and 4.1% for KC, respectively. Finally, adding two bands (550 nm and 800 nm with 25 nm of FWHM) in four bands with 50 nm of FWHM have been proposed to improve the usability of multispectral image sensors with performing various roles in agriculture as well as detecting FB with other combinations of spectral bands.