光谱学与光谱分析 |
|
|
|
|
|
Prediction of SPAD Value in Oilseed Rape Leaves Using Hyperspectral Imaging Technique |
DING Xi-bin, LIU Fei, ZHANG Chu, HE Yong* |
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
Abstract In the present work, prediction models of SPAD value (Soil and Plant Analyzer Development, often used as a parameter to indicate chlorophyll content) in oilseed rape leaves were successfully built using hyperspectral imaging technique. The hyperspectral images of 160 oilseed rape leaf samples in the spectral range of 380~1030 nm were acquired.Average spectrum was extracted from the region of interest(ROI) of each sample.We chose spectral data in the spectral range of 500~900 nm for analysis. Using Monte Carlo partial least squares(MC-PLS) algorithm, 13 samples were identified as outliers and eliminated. Based on the spectral information and measured SPAD values of the rest 147 samples, several estimation models have been built based on different parameters using different algorithms for comparison,including: (1) a SPAD value estimation model based on partial least squares(PLS) in the whole wavelength region of 500~900 nm; (2) a SPAD value estimation model based on successive projections algorithmcombined with PLS(SPA-PLS); (3) 4 kind of simple experience SPAD value estimation models in which red edge position was used as an argument; (4) 4 kind of simple experience SPAD value estimation models in which three vegetation indexes R710/R760,(R750-R705)/(R750-R705) and R860/(R550×R708), which all have been proved to have a good relevance with chlorophyll content, were used as an argument respectively; (5) a SPAD value estimation model based on PLS using the 3 vegetation indexes mentioned above. The results indicate that the optimal prediction performance is achieved by PLS model in the whole wavelength region of 500~900 nm, which has a correlation coefficient(rp) of 0.833 9 and a root mean squares error of predicted(RMSEP) of 1.52. The SPA-PLS model can provide avery close prediction result while the calibration computation has been significantly reduced and the calibration speed has been accelerated sharply. For simple experience models based on red edge parameters and vegetation indexes, although there is a slight gap between theprediction performance and that of the PLS model in the whole wavelength region of 500~900 nm, they also have their own unique advantages which should be thought highly of: these models are much simpler and thus the calibration computation is reduced significantly, they can perform an important function under circumstances in which increasing modeling speed and reducing calibration computation operand are more important than improving the prediction accuracy, such as the development of portable devices.
|
Received: 2013-11-21
Accepted: 2014-02-20
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] Vane G, Alexander F H G. Remote Sensing of Environment, 1993, 44(2): 117. [2] Scott C C, Hector J B. Agronomy Journal, 1997, 89(4): 557. [3] Peng S, Garcia F V, Laza R C, et al. Agronomy Journal, 1993, 85(5): 987. [4] Horler D N H, Barber J, Barringer A R. International Journal of Remote Sensing, 1980, 1(2): 121. [5] Curran P J, Dungan J L, Gholz H L. Tree Physiology, 1990, 7(1-4): 33. [6] Zou X, Shi J, Hao L, et al. Analytica Chimica Acta, 2011, 706: 105. [7] LIU Fei, NIE Peng-cheng, HE Yong, et al.(刘 飞, 聂鹏程, 何 勇, 等). Science China Information Sciences(中国科学:信息科学), 2011, 54(3): 598. [8] Guo W L, Du Y P, Zhou Y C, et al. World J. Microbiol. Biotechnol., 2012, 28: 993. [9] David M H, Edward V T. Analytical Chemistry, 1988, 60(11): 1193. [10] Liu F, Kong W W, He Y. Sensor Letters, 2011, 9(3): 1126. [11] Araujo M C U, Saldanha T C B, Galvao R K H, et al. Chemometr. Intell. Lab., 2001, 57(2): 65. [12] QIAN Hai-bo, SUN Lai-jun, WANG Le-kai, et al(钱海波, 孙来军, 王乐凯, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2011, 27(18): 51. [13] YAO Fu-qi, ZHANG Zhen-hua, YANG Run-ya, et al(姚付启, 张振华, 杨润亚, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(Supp. 2): 123. [14] HUANG Wen-jiang, WANG Ji-hua, ZHAO Chun-jiang, et al(黄文江, 王纪华, 赵春江, 等). Remote Sensing Technology and Application(遥感技术与应用), 2003, 18(4): 206. [15] ZHU Xi-cun, ZHAO Geng-xing, JIANG Yuan-mao, et al(朱西存, 赵庚星, 姜远茂, 等). Infrared(Monthly)(红外) , 2011, 32(12): 31. [16] Daniel A S, John A G. Remote Sensing of Environment, 2002, 81: 337. [17] ZHANG Lian-peng, LIU Qin-huo, WANG De-gao, et al(张连蓬, 柳钦火, 王德高, 等). Bulletin of Surveying and Mapping(测绘通报), 2010, 9: 1.
|
[1] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[2] |
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. |
[3] |
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. |
[4] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
[5] |
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. |
[6] |
ZHENG Shu-yuan1, 2, HAI Yan1, 2, HE Meng-qi1, 2, WANG Jian-xiong1, 2. Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3509-3517. |
[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] |
FU Xiao-man1, 2, BAO Yu-long1, 2*, Bayaer Tubuxin1, 2, JIN Eerdemutu1, 2, BAO Yu-hai1, 2. Spectral Characteristics Analysis of Desert Steppe Vegetation Based on Field Online Multi-Angle Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3170-3179. |
[10] |
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. |
[11] |
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. |
[12] |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*. Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2627-2637. |
[13] |
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. |
[14] |
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. |
[15] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
|
|
|
|