Detecting Drought Stress in Soybean Plants Using Hyperspectral Fluorescence Imaging

다국어 초록 (Multilingual Abstract)

Purpose: Soybean growth is adversely affected by environmental stresses such as drought, extreme temperatures, and nutrient deficiency. The objective of this study was to develop a method for rapid measurement of drought stress in soybean plants using a hyperspectral fluorescence imaging technique. Methods: Hyperspectral fluorescence images were obtained using UV-A light with 365 nm excitation. Two soybean cultivars under drought stress were analyzed. A partial least square regression (PLSR) model was used to predict drought stress in soybeans. Results: Partial least square (PLS) images were obtained for the two soybean cultivars using the results of the developed model during the period of drought stress treatment. Analysis of the PLS images showed that the accuracy of drought stress discrimination in the two cultivars was 0.973 for an 8-day treatment group and 0.969 for a 6-day treatment group. Conclusions: These results validate the use of hyperspectral fluorescence images for assessing drought stress in soybeans.

참고문헌 (Reference)

1 장안철, "기후변화에 대응한 농업생명공학의 기회와 도전" 한국식물생명공학회 38 (38): 117-124, 2011

2 Caires, A. R. L., "Water Stress Response of Conventional and Transgenic Soybean Plants Monitored by Chlorophyll a Fluorescence" 20 : 645-649, 2010

3 Manickavasagan, A., "Thermal imaging to detect infestation by cryptolestes ferrugineus inside wheat kernels" 44 : 186-192, 2008

4 Shibayama, M., "Regression-based models to predict rice leaf area index using biennial fixed point continuous observations of near infrared digital images" 14 : 365-376, 2011

5 Buschmann, C., "Principles and characteristics of multi-colour fluorescence imaging of plants" 152 : 297-314, 1998

6 Gross, J., "Pigments in Vegetables: Chlorophylls and Carotenoids" Van Nostrand Reinhold 1991

7 Houle, D., "Phenomics : the next challenge" 11 (11): 855-866, 2010

8 Gitelson, A. A., "Leaf chlorophyll fluorescence corrected for reabsorption by means of absorption and reflectance measurements" 152 : 283-296, 1998

9 Kim, M. S., "Hyperspectral reflectance and fluorescence imaging system for food quality and safety" 44 (44): 721-729, 2001

10 Rousseau, C., "High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis" 9 : 17-, 2013

11 Dahn, H. G., "Characterisation of drought stress of maize and wheat canopies by means of spectral resolved laser induced fluorescence" 1 : 12-19, 1992

12 Nguyen, H. T., "Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression" 24 : 349-356, 2006

13 Rahaman, Md. M., "Advanced phenotyping and phenotype data analysis for the study of plant growth and development" 6 (6): 1-15, 2015

14 Woo, N. S., "A rapid, non-invasive procedure for quantitative assessment of drought survival using chlorophyll fluorescence" 4 : 27-, 2008

15 Duan, L. F., "A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice" 7 : 44-, 2011