| 光谱学与光谱分析 |
|
|
|
|
|
| Rapid Diagnosis of Sound, Yellow and Citrus Greening Leaves with Hyperspectral Imaging |
| SUN Xu-dong1, LIU Yan-de1*, XIAO Huai-chun1, ZHANG Zhi-cheng1, LI Ze-min1, Lü Qiang2 |
| 1. School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China2. Citrus Research Institute, Chinese Academy of Agricultural Sciences, Chongqing 400712, China |
|
|
|
|
Abstract The feasibility was investigated for identifying sound, yellow and citrus greening leaves of navel orange trees based on hyperspectral imaging combined with correlation analysis and discriminant partial least square (DPLS) methods. The hyperspectral data of sound, yellow and citrus greening leaves were recorded in the wavelength range of 374.28～1 016.89 nm. Two regions of interest (ROI) were marked symmetrically on both sides along main veins with an ellipse of major axis of 60 pixels and minor axis of 30 pixels. The average reflectance spectrum was extracted from ROI regions. A pair wavelengths of 502.79 and 374.28 nm were chosen with correlation analysis method in the wavelength range of 374.28～1 016.89 nm. The classification model was developed with the peak ratio of the pair wavelengths. This model was effective to sound leaves with the recognition accuracy of 1.7% but yellow and citrus greening leaves. The DPLS model was employed with the preprocessing spectra of second derivative and Savitzky-Golay smoothing. The recognition accuracy of this model was 100% for citrus greening leaves and yellow ones. The number of latent variables (LVs) was optimized with the leave one out cross validation method. The optimal LVs, correlation coefficient and standard error of calibration of the DPLS model were 17, 0.96 and 0.13, respectively. The correction classification rate of the DPLS model was 100% for yellow leaves and citrus greening ones. Two-step method of the peak ratio models combination with the DPLS was proposed for identifying sound, yellow and citrus greening leaves. The new samples were applied to evaluation the classification ability of the two-step method, which included sound leaves of 10, citrus greening leaves of 10 and yellow leaves of 10. The correction classification rate reached 96.7%. Experimental results showed that it was feasible to identify sound, yellow and citrus greening leaves by hyperspectral imaging coupled with the peak ratio and DPLS models.
|
|
Received: 2015-09-29
Accepted: 2016-02-12
|
|
|
|
Corresponding Authors:
LIU Yan-de
E-mail: jxliuyd@163.com
|
|
[1] BOVé J M. Phytoparasitica, 2014, 42: 579.
[2] Deng X L, Gao Y D, Chen J C, et al. Journal of Integrative Agriculture, 2012, 11(3): 424.
[3] Bove J M. Journal of Plant Pathology, 2006, 88(1): 7.
[4] LI Xiu-hua, LI Min-zan, LEE W S, et al(李修华，李民赞，LEE W S，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014，34(6)：1553.
[5] LI X H, LEE W S, LI M Z, et al. Computers and Electronics in Agriculture, 2012, 83: 32.
[6] MA Hao, JI Hai-yan, LEE W S(马 淏，吉海彦，LEE W S). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(10): 2713.
[7] MEI Hui-lan, DENG Xiao-ling, HONG Tian-sheng, et al(梅慧兰，邓小玲，洪添胜，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(9): 140.
[8] Garcia-Ruiz F, Sankaran S, Maja J M, et al. Computers and Electronics in Agriculture, 2013, 91: 106.
[9] Sankaran S, Ehsani R. Crop Protection, 2011, 30: 1508.
[10] Mishra A, Karimi D, Ehsani R, et al. Biosystems Engineering, 2011, 110: 302.
[11] Pourreza A, Lee W S, Etxeberria E, et al. Biosystems Engineering, 2015, 130: 13.
[12] Sankaran S, Ehsani R, Etxeberria E. Talanta, 2010, 83: 574.
[13] Lee K, Kang S, Delwiche S R, et al. Sens. & Instrument. Food. Qual., 2008, 2: 90. |
| [1] |
CAO Xiao-feng, REN Hui-ru, LI Xing-zhi, YU Ke-qiang*, SU Bao-feng*. Discrimination of Winter Jujube’s Maturity Using Hyperspectral Technique Combined with Characteristic Wavelength and Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2175-2182. |
| [2] |
HUANG Yu-ping1,3, Renfu Lu2, QI Chao1, CHEN Kun-jie1*. Tomato Maturity Classification Based on Spatially Resolved Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2183-2188. |
| [3] |
HUANG Di-yun, LI Jing-bin*, YOU Jia, KAN Za. The Classification of Delinted Cottonseeds Varieties by Fusing Image Information Based on Hyperspectral Image Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2227-2232. |
| [4] |
XIE Ya-ping1, CHEN Feng-nong1, ZHANG Jing-cheng1, ZHOU Bin2, WANG Hai-jiang3, WU Kai-hua1*. Study on Monitoring of Common Diseases of Crops Based on Hyperspectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2233-2240. |
| [5] |
GAO Pan1, ZHANG Chu3, Lü Xin2*, ZHANG Ze2, HE Yong3*. Visual Identification of Slight-Damaged Cotton Seeds Based on Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1712-1718. |
| [6] |
CHEN Bing1, WANG Gang4, LIU Jing-de1*, MA Zhan-hong2, WANG Jing3, LI Tian-nan1, 2. Extraction of Photosynthetic Parameters of Cotton Leaves under Disease Stress by Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1834-1838. |
| [7] |
TAO Chao1*, WANG Ya-jin1, ZOU Bin1, 2, TU Yu-long1, JIANG Xiao-lu1. Assessment and Analysis of Migrations of Heavy Metal Lead and Zinc in Soil with Hyperspectral Inversion Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1850-1855. |
| [8] |
YAN Meng-ge1,3, DONG Xiao-zhou1,3, LI Ying2, ZHANG Ying2, BI Yun-feng1,3*. Classification of Geological Samples with Laser-Induced Breakdown Spectroscopy Based on Self-Organizing Feature Map Network and Correlation Discrimination Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1874-1879. |
| [9] |
WANG Chao, WANG Jian-ming, FENG Mei-chen, XIAO Lu-jie, SUN Hui, XIE Yong-kai, YANG Wu-de*. Hyperspectral Estimation on Growth Status of Winter Wheat by Using the Multivariate Statistical Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1520-1525. |
| [10] |
CAO Xiao-lan1, 2, CHEN Xing-ming2, ZHANG Shuai2, CUI Guo-xian1*. Ramie Variety Identification Based on the Hyperspectral Parameters and the Stepwise Discriminant Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1547-1551. |
| [11] |
LI Shuang-fang1,2, GUO Yu-bao1*, SUN Yan-hui2, GU Hai-yang2. Rapid Identification of Sunflower Seed Oil Quality by Three-Dimensional Synchronous Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1165-1170. |
| [12] |
WANG Xiao-bin1, 2, 3, 4, 5，HUANG Wen-qian2, 3, 4, 5，WANG Qing-yan2, 3, 4, 5，LI Jiang-bo2, 3, 4, 5，WANG Chao-peng2, 3, 4, 5，ZHAO Chun-jiang1, 2, 3, 4, 5*. Application of Near Infrared Hyperspectral Imaging for Detection of Azodicarbonamidein Flour[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 805-812. |
| [13] |
TU Yu-long, ZOU Bin*, JIANG Xiao-lu, TAO Chao, TANG Yu-qi, FENG Hui-hui. Hyperspectral Remote Sensing Based Modeling of Cu Content in Mining Soil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 575-581. |
| [14] |
LIU Yan-de1, XIAO Huai-chun1, SUN Xu-dong1, HAN Ru-bing1, YE Ling-yu1, HUANG Liang1, XIAO Yu-song1, LIAO Xiao-hong2. Study on the Quick Non-Destructive Detection of Citrus Huanglongbing Based on the Spectrometry of VIS and NIR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 528-534. |
| [15] |
QIAO Xiao-jun, JIANG Jin-bao*, LI Hui, QI Xiao-tong, YUAN De-shuai. College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 535-539. |
|
|
|
|
