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초분광 이미지를 이용한 배나무 화상병에 대한 최적 분광 밴드 선정

Authors
강예성박준우장시형송혜영강경석유찬석김성헌전새롬강태환김국환
Issue Date
Mar-2021
Publisher
한국농림기상학회
Citation
한국농림기상학회지, v.23, no.1, pp 15 - 33
Pages
19
Indexed
KCI
Journal Title
한국농림기상학회지
Volume
23
Number
1
Start Page
15
End Page
33
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/3984
ISSN
1229-5671
2288-1859
Abstract
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.
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Ryu, Chan Seok
농업생명과학대학 (생물산업기계공학과)
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