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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #401222

Research Project: Advancement of Sensing Technologies for Food Safety and Security Applications

Location: Environmental Microbial & Food Safety Laboratory

Title: Fluorescence hyperspectral imaging for early diagnosis of heat-stressed ginseng plants

item FAQUURZADA, MOHAMMAD - Chungnam National University
item PARK, EUNSOO - Chungnam National University
item KIM, TAEHYUN - Korean Rural Development Administration
item Kim, Moon
item Baek, Insuck
item JOSHI, RAHUL - Chungnam National University
item KIM, JUNTAE - Chungnam National University
item CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Applied Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/15/2022
Publication Date: 12/20/2022
Citation: Faquurzada, M.A., Park, E., Kim, T., Kim, M.S., Baek, I., Joshi, R., Kim, J., Cho, B. 2023. Fluorescence hyperspectral imaging for early diagnosis of heat-stressed ginseng plants. Applied Sciences. 13:31.

Interpretive Summary: Ginseng is a highly valued perennial herbaceous plant widely used for traditional medicine and as a functional food supplement. Significant adverse effects on ginseng plants from high temperatures have been reported, including reduced growth and reduced ginsenoside and phenolic compositions that can lower both market value and functionality. Increasing heat waves and climate change are prompting a need for methods to effectively and non-destructively detect early stages of heat stress in ginseng plants to enable effective, timely responses to mitigate non-optimal environmental conditions and minimize their adverse impacts. Researchers investigated fluorescence imaging methods to nondestructively assess chlorophyll composition—a measure of heat stress—of the leaves of live ginseng plants, including two temperature-sensitive varieties and two temperature-resistant varieties subjected to heat stress under controlled environment conditions. The research results demonstrated 98% accuracy in detecting and predicting chlorophyll composition, suggesting that the spectral wavebands identified for the test methods could be effectively used to develop portable, low-cost instruments for early-stage evaluation of ginseng plants for heat stress. The use of such instruments could greatly benefit producers of ginseng and other temperature-sensitive crops in maintaining high quality production to meet consumer demand in the face of adverse stresses caused by climate change.

Technical 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 the 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 R2 val = 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.