|
|
|
|
|
|
Quantified Estimation of Anthocyanin Content in Mosaic Virus Infected Apple Leaves Based on Hyperspectral Imaging |
TIAN Ming-lu1,2,3, BAN Song-tao1, CHANG Qing-rui1*, ZHANG Zhuo-ran1, WU Xu-mei1, WANG Qi1 |
1. College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
2. Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
3. Shanghai Engineering Research Center for Digital Agriculture, Shanghai 201403, China |
|
|
Abstract Anthocyanin has the antioxidant effect, which is helpful to the recover of leaf injury. The dynamic change of anthocyanin concentration can be considered as a sensitive indicator to reflect plant physiological conditions affected by external environmental stresses, consequently the anthocyanin content of mosaic virus infecting apple leaves can be used as an important criterion for assessing the degree of disease. In this research, hyperspectral images of apple leaves with mosaic disease were acquired by imaging spectrometer. By the combination of each two bands, spectral reflectance was used to establish the optimal spectral indexes which were highly correlated to the anthocyanin content in infected leaves. Further more, a accurate anthocyanin concentration estimation model was established taking these spectral indexes as parameters. The results were as follows: (1) The damage of mesophyll cells in mosaic virus infecting apple leaves would cause the increase of anthocyanin content. As a result, the spectral reflectance of infected area increased significantly in visible region, while in near infrared region the reflectance was lower than the normal. In addition, the red-edge position shifted to the shorter wavelength and both the red-edge area and the first derivative spectral reflectance at red-edge position decreased. (2) The correlation between leaf anthocyanin content and spectral reflectance was extremely significant at most of the wavebands and reached the peak at 581 nm. The normalised deviation spectral index combined with spectral reflectance at 770 and 722 nm, the simple ratio spectral index combined with spectral reflectance at 717 and 770 nm and the deviation spectral index correlated with spectral reflectance at 581 and 520 nm were all significantly related to leaf anthocyanin content, with the correlation coefficent of 0.838, 0.865 and 0.875, respectively. (3) Anth-PLSR was the optimal model to estimate apple leaf anthocyanin content, of which the determination coefficient was 0.823 and RMSE was 0.056. The anthocyanin content distribution diagram of leaves were made by solving the hyperspectral images pixel using Anth-PLSR model, thus the anthocyanin content of an integral leaf was calculated. On the other hand, by extracting the average spectral reflectance from the the hyperspectral image of a whole leaf, the anthocyanin content of the integral leaf can be obtained using Anth-PLSR model. The results of these two different methods showed a high consistence by fitting analysis, which demonstrated that the latter method could be used to rapidly detect the anthocyanin content of apple leaves.
|
Received: 2016-11-16
Accepted: 2017-03-26
|
|
Corresponding Authors:
CHANG Qing-rui
E-mail: changqr@nwsuaf.edu.cn
|
|
[1] Grimová L, Winkowska L, Konrady M, et al. Phytopathologia Mediterranea, 2016, 55(1): 1.
[2] Cies′lińska M, Valasevich N. Journal of Plant Diseases and Protection, 2016, 123(4): 187.
[3] Ahmed N U, Park J, Jung H, et al. Functional & Integrative Genomics, 2015, 15(4): 383.
[4] LIU Xiu-ying, SHEN Jian, CHANG Qing-rui, et al(刘秀英,申 健,常庆瑞, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015,(9): 319.
[5] Kovinich N, Kayanja G, Chanoca A, et al. Plant Signaling & Behavior, 2015, 10(7).
[6] Wu Q, Wang J, Wang C, et al. Infrared Physics & Technology, 2016, 78: 66.
[7] HU Yao-hua, PING Xue-wen, XU Ming-zhu, et al(胡耀华,平学文,徐明珠, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(2): 515.
[8] Iori A, Scala V, Cesare D, et al. European Journal of Plant Pathology, 2015, 141(4): 689.
[9] Zhang D, Lin F, Huang Y, et al. International Journal of Agriculture & Biology, 2016, 18(4): 747.
[10] CHENG Zhi-qing, ZHANG Jin-song, MENG Ping, et al(程志庆,张劲松, 孟 平, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015,31(12): 179.
[11] Inoue Y, Sakaiya E, Zhu Y, et al. Remote Sensing of Environment, 2012, 126: 210.
[12] ZHANG Dong-yan, LIU Liang-yun, HUANG Wen-jiang, et al(张东彦,刘良云,黄文江, 等). Infrared and Laser Engineering(红外与激光工程), 2013, 42(7): 1871.
[13] Abdel-Rahman E M, Mutanga O, Odindi J, et al. Computers and Electronics in Agriculture, 2014, 106: 11.
[14] Prabhakar M, Prasad Y G, Desai S, et al. Crop Protection, 2013, 45(3): 132. |
[1] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[2] |
YANG Guang1, JIN Chun-bai1, REN Chun-ying2*, LIU Wen-jing1, CHEN Qiang1. Research on Band Selection of Visual Attention Mechanism for Object
Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 266-274. |
[3] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
[4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[5] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[6] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[7] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[8] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[9] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
[10] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
[11] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
[12] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
[13] |
HU Wen-feng1, 2, TANG Wei-hao1, LI Chuang1, WU Jing-jin1, MA Qing-fen1, LUO Xiao-chuan1, WANG Chao2, TANG Rong-nian1*. Estimating Nitrogen Concentration of Rubber Leaves Based on a Hybrid Learning Framework and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2050-2058. |
[14] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
[15] |
LIU Gang1, LÜ Jia-ming1, NIU Wen-xing1, LI Qi-feng2, ZHANG Ying-hu2, YANG Yun-peng2, MA Xiang-yun2*. Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1697-1702. |
|
|
|
|