The objective of this study is to measure the growth status of garlic crops in open-field using VIS/NIR hyperspectral imaging system. A hyperspectral imaging system capable of acquiring a wavelength of 400 nm to 1000 nm was used, and the hyperspectral image data were analyzed by PLSR (Partial Least Square Regression), LS-SVM (Least Square Support Vector Machine), CNN (Convolutional Neural). Networks) and Spatial-Spectral Residual network (SSRN). The optimal model was able to classify the difference by fertilization levels with an accuracy of 80 to 99%, and the difference by soil covering with an accuracy of 93-99. These results show that the Vis/NIR hyperspectral imaging system and data can be utilized to predict the growth status of garlic.
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