Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging

• Published in: International journal of molecular sciences 2023-03, Vol.24 (6), p.5825
• Main Authors: Yang, Yucun, Nan, Rui, Mi, Tongxi, Song, Yingxin, Shi, Fanghui, Liu, Xinran, Wang, Yunqi, Sun, Fengli, Xi, Yajun, Zhang, Chao
• Format: Article
• Language: English
• Subjects: Abiotic stress; Chlorophyll; Correlation analysis; Drought; Drought resistance; Droughts; Food security; Food supply; Genetic analysis; Genetic diversity; Hyperspectral Imaging; Leaves; Light; Machine learning; Nondestructive testing; Photosynthesis; Plant Breeding; Plant Leaves; Plants (botany); Principal components analysis; Reflectance; Triticum - genetics; Water shortages; Wheat; Wheat industry
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24065825

Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550-700 nm region. Hyperspectral reflectance at 549 nm (r = -0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R , 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.