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Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants

Authors :
Mohammad Akbar Faqeerzada
Eunsoo Park
Taehyun Kim
Moon Sung Kim
Insuck Baek
Rahul Joshi
Juntae Kim
Byoung-Kwan Cho
Source :
Applied Sciences, Vol 13, Iss 1, p 31 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Ginseng is a perennial herbaceous plant that has been widely consumed for medicinal and dietary purposes since ancient times. Ginseng plants require shade and cool temperatures for better growth; climate warming and rising heat waves have a negative impact on the plants’ productivity and yield quality. Since Republic of Korea’s temperature is increasing beyond normal expectations and is seriously threatening ginseng plants, an early-stage non-destructive diagnosis of stressed ginseng plants is essential before symptomatic manifestation to produce high-quality ginseng roots. This study demonstrated the potential of fluorescence hyperspectral imaging to achieve the early high-throughput detection and prediction of chlorophyll composition in four varieties of heat-stressed ginseng plants: Chunpoong, Jakyeong, Sunil, and Sunmyoung. Hyperspectral imaging data of 80 plants from these four varieties (temperature-sensitive and temperature-resistant) were acquired before and after exposing the plants to heat stress. Additionally, a SPAD-502 meter was used for the non-destructive measurement of the greenness level. In accordance, the mean spectral data of each leaf were extracted from the region of interest (ROI). Analysis of variance (ANOVA) was applied for the discrimination of heat-stressed plants, which was performed with 96% accuracy. Accordingly, the extracted spectral data were used to develop a partial least squares regression (PLSR) model combined with multiple preprocessing techniques for predicting greenness composition in ginseng plants that significantly correlates with chlorophyll concentration. The results obtained from PLSR analysis demonstrated higher determination coefficients of R2val = 0.90, and a root mean square error (RMSE) of 3.59%. Furthermore, five proposed bands (683 nm, 688 nm, 703 nm, 731 nm, and 745 nm) by stepwise regression (SR) were developed into a PLSR model, and the model coefficients were used to create a greenness-level concentration in images that showed differences between the control and heat-stressed plants for all varieties.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.06273bdef55483fb7a10a86d68a2c47
Document Type :
article
Full Text :
https://doi.org/10.3390/app13010031