| 光谱学与光谱分析 |
|
|
|
|
|
| Measurement of Chlorophyll Content in Wheat Leaves Using Hyperspectral Scanning |
| HUANG Hui，WANG Wei，PENG Yan-kun*, WU Jian-hu，GAO Xiao-dong，WANG Xiu，ZHANG Jing |
| College of Engineering，China Agricultural University，Beijing 100083，China |
|
|
|
|
Abstract The objective of the present research was to evaluate the potential of hyperspectral scanning as a way for nondestructive measurement of chlorophyll content in wheat leaves, which can indicates the plant healthy status. One hundred twenty samples were randomly picked from Xiao Tangshan farm. Ninety samples were used as calibration set and others were used for verification set. After capturing hyperspectral image in the range of 400-1 000 nm，the chlorophyll contents of samples were measured immediately. Four different mathematical treatments were used in spectra processing in the wavelength range of 491-887 nm：multiplicative scatter correction (MSC)，first derivative correction，and second derivative correction. Statistical models were developed using partial least square regression (PLSR)，and stepwise multiple linear regression (SMLR) analysis technique. The results showed that the best calibration model was obtained by PLSR analysis，after processing spectra with MSC and second derivate, with a relatively higher coefficient of determination of calibration (0.82) and validation (0.79) respectively, a relatively lower RMSEC value (0.69), and a small difference between RMSEC (0.69) and RMSEP (0.71). The results indicate that it is feasible to use hyperspectral scanning technique for nondestructive measurement of chlorophyll content in wheat leaves.
|
|
Received: 2009-08-08
Accepted: 2009-11-12
|
|
|
|
Corresponding Authors:
PENG Yan-kun
E-mail: ypeng@cau.edu.cn
|
|
[1] YUAN Jin-guo，NIU Zheng(袁金国，牛 铮). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2007，23(4)：172.
[2] Chang S X，Robison D J. Forest Ecol. Manag，2003，181：331.
[3] Lopez-Bellido R J，Shepherd C E，Barraclough P B. Eur. J. Agron.，2004，20：313.
[4] Li X L，He Y. Horticultural Science，2008，43(5)：1586.
[5] QIU Zheng-jun，SONG Hai-yan，HE Yong，et al(裘正军，宋海燕，何 勇，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2007，23(7)：150.
[6] Noh H，Zhang Q，Shin B，et al. Biosystems Eng.，2006，94(4)：477.
[7] NIU Xiao-ying，QIAN Dong-ping，WANG Xiu(牛晓颖，钱东平，王 秀). Journal of Agricultural University of Hebei(河北农业大学学报)，2007，30(6)：1455.
[8] Peng Y，Lu R. Transactions of the ASABE，2006，49(1)：269.
[9] Peng Y，Lu R. Post Harvest Biology and Technology，2008，48：52.
[10] WANG Ji-hua，HUANG Wen-jiang，LAO Cai-lian, et al(王纪华，黄文江，劳彩莲，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2007，27(7)：1319.
[11] Prasad S T，Ronald B S，Eddy D P. Remote Sensing of Environment，2000，71：158.
[12] Ceccato P，Flasse S，Gregoire J M. Remote Sensing of Environment，2002，82：198.
|
| [1] |
ZHU Wen-jing1, 2，FENG Zhan-kang1, 2，DAI Shi-yuan1, 2，ZHANG Ping-ping3，JI Wen4，WANG Ai-chen1, 2，WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
| [2] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [3] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [4] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [5] |
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. |
| [6] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [7] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [8] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [9] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [10] |
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. |
| [11] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [12] |
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. |
| [13] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [14] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
| [15] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
|
|
|
|
