Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging
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 an...
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MDPI AG
2023-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/6/5825 |
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| author | Yucun Yang Rui Nan Tongxi Mi Yingxin Song Fanghui Shi Xinran Liu Yunqi Wang Fengli Sun Yajun Xi Chao Zhang |
| author_facet | Yucun Yang Rui Nan Tongxi Mi Yingxin Song Fanghui Shi Xinran Liu Yunqi Wang Fengli Sun Yajun Xi Chao Zhang |
| author_sort | Yucun Yang |
| collection | DOAJ |
| description | 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<sup>2</sup>, 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. |
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| language | English |
| last_indexed | 2024-03-11T06:24:44Z |
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| series | International Journal of Molecular Sciences |
| spelling | doaj.art-18e4c01c5fd944dfb0e85af47f72cdb42023-11-17T11:39:37ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-03-01246582510.3390/ijms24065825Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral ImagingYucun Yang0Rui Nan1Tongxi Mi2Yingxin Song3Fanghui Shi4Xinran Liu5Yunqi Wang6Fengli Sun7Yajun Xi8Chao Zhang9State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, ChinaChlorophyll 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<sup>2</sup>, 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.https://www.mdpi.com/1422-0067/24/6/5825wheat leaves chlorophylldrought stressmachine learningregression modelhigh-resolution spectral imaginghigh-throughput phenotypic identification |
| spellingShingle | Yucun Yang Rui Nan Tongxi Mi Yingxin Song Fanghui Shi Xinran Liu Yunqi Wang Fengli Sun Yajun Xi Chao Zhang Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging International Journal of Molecular Sciences wheat leaves chlorophyll drought stress machine learning regression model high-resolution spectral imaging high-throughput phenotypic identification |
| title | Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging |
| title_full | Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging |
| title_fullStr | Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging |
| title_full_unstemmed | Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging |
| title_short | Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging |
| title_sort | rapid and nondestructive evaluation of wheat chlorophyll under drought stress using hyperspectral imaging |
| topic | wheat leaves chlorophyll drought stress machine learning regression model high-resolution spectral imaging high-throughput phenotypic identification |
| url | https://www.mdpi.com/1422-0067/24/6/5825 |
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