In this research, the ground based hyperspectral reflectance of Chinese cabbage and radish depending on the vegetation growth stages was compared to each other. The classifiers namely decision tree, random forest and support vector machine were tested to check the feasibility of classification depending on the difference in hyperspectral reflectance. The ability of classifier was compared with the overall accuracy and kappa coefficient depending on the vegetation growth stages. The spectral merging was applied to find out the optimal spectral bands to make new multispectral sensor based on the commercial band pass filter with full width at half maximum (FWHM) such as 10nm, 25nm, 40nm, 50nm and 80nm. It was ascertained that the pattern of hyperspectral reflectance varied in Chinese cabbage and radish and also found a certain disparity of pattern in different vegetation growing stage. Although the classifying ability of support vector machine with linear method was higher than the other six methods, it was not suitable for new multispectral sensor. Hence, the decision tree with Rpart method is advantageous as a best classifier to make new multispectral sensor in order to separate the hyperspectral reflectance of Chinese cabbage and radish depending on the vegetation growth stages. The substantiates two alternative aggregate of bands 410nm, 430nm, 700nm and 720nm with 10nm of FWHM or 410nm, 440nm, 690nm and 720nm with 25nm of FWHM were suggested to be the best combinations to make new multispectral sensor without the overlap of FWHM.
Hyperspectral camera was applied to establish the models of catechin concentration for green tea. The possibility of
improvement for the models was checked by the multi-year models and the mutual prediction. ECg, EGCg and the ester
catechin (ECg and EGCg) decreased with the growth but EC, EGC and the free catechin (EC and EGC) were changed by
the covering. In partial least square regression (PLSR) models for each catechin, R2 (Relative Error for validation) was
more than 0.785 (13.4%) for a single year data, 0.723 (13.3%) for two years data, and 0.756 (13.6%) for three years data
except several catechins. It was possible to improve the precision and accuracy of models using the combination of
catechin (free and ester type) or the combination of multi-year data. When each and each type of catechin model was
predicted by the other year data, the accuracy of two years model improved comparing with it of a single year data. It
means that the multi-year models might be more accurate than a single year models to predict the unknown data.
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