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] |
WANG Jin1, 2, CHEN Shu-tao1, 2*, DING Si-cheng1, 2, YAO Xue-wen1, 2, ZHANG Miao-miao1, 2, HU Zheng-hua2. Relationships Between the Leaf Respiration of Soybean and Vegetation
Indexes and Leaf Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1607-1613. |
[2] |
ZHANG Jun-yi1, 2, GAO De-hua1, SONG Di1, QIAO Lang1, SUN Hong1, LI Min-zan1*, LI Li1. Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1514-1521. |
[3] |
JIANG Xiao-yu1, 2, LI Fu-sheng2*, WANG Qing-ya1, 2, LUO Jie3, HAO Jun1, 2, XU Mu-qiang1, 2. Determination of Lead and Arsenic in Soil Samples by X Fluorescence Spectrum Combined With CARS Variables Screening Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1535-1540. |
[4] |
ZHANG Tian-liang, ZHANG Dong-xing, CUI Tao, YANG Li*, XIE Chun-ji, DU Zhao-hui, ZHONG Xiang-jun. Identification of Early Lodging Resistance of Maize by Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1229-1234. |
[5] |
LI Lian-jie1, 2, FAN Shu-xiang2, WANG Xue-wen1, LI Rui1, WEN Xiao1, WANG Lu-yao1, LI Bo1*. Classification Method of Coal and Gangue Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1250-1256. |
[6] |
SHI Ji-yong, LIU Chuan-peng, LI Zhi-hua, HUANG Xiao-wei, ZHAI Xiao-dong, HU Xue-tao, ZHANG Xin-ai, ZHANG Di, ZOU Xiao-bo*. Detection of Low Chromaticity Difference Foreign Matters in Soy Protein Meat Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1299-1305. |
[7] |
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan*. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097. |
[8] |
JI Tong1, 2, WANG Bo1, 2, YANG Jun-ying1, 2, LI Qiang1, 2, HE Guo-xing1, 2, PAN Dong-rong3, LIU Xiao-ni1, 2*. Spectral Characteristic Analysis and Spectral Identification of Desert Plants in Yanchi, Ningxia[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 678-685. |
[9] |
FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, YUAN Rui-rui, SUN You-rui, LI Yue. Rapid Determination of TBARS Contents in Tan Mutton Using Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 713-718. |
[10] |
QIAO Lu, WANG Song-lei*, GUO Jian-hong, HE Xiao-guang. Combination of Spectral and Textural Informations of Hyperspectral Imaging for Predictions of Soluble Protein and GSH Contents in Mutton[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 176-183. |
[11] |
YE Rong-ke1, KONG Qing-chen1, LI Dao-liang1, 2, CHEN Ying-yi1, 2, ZHANG Yu-quan1, LIU Chun-hong1, 2*. Shrimp Freshness Detection Method Based on Broad Learning System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 164-169. |
[12] |
ZHANG Hui-jie, CAI Chong*, CUI Xu-hong, ZHANG Lei-lei. Rapid Detection of Anthocyanin in Mulberry Based on Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3771-3775. |
[13] |
DU Meng-meng1, Ali Roshanianfard2, LIU Ying-chao3. Inversion of Wheat Tiller Density Based on Visible-Band Images of Drone[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3828-3836. |
[14] |
WU Ye-lan1, CHEN Yi-yu1, LIAN Xiao-qin1, LIAO Yu2, GAO Chao1, GUAN Hui-ning1, YU Chong-chong1. Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3837-3843. |
[15] |
OUYANG Ai-guo, WAN Qi-ming, LI Xiong, XIONG Zhi-yi, WANG Shun, LIAO Qi-cheng. Research on Rich Borer Detection Methods Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3844-3850. |
|
|
|
|