光谱学与光谱分析 |
|
|
|
|
|
Study of Building Quantitative Analysis Model for Chlorophyll in Winter Wheat with Reflective Spectrum Using MSC-ANN Algorithm |
LIANG Xue1,JI Hai-yan1*, WANG Peng-xin1, RAO Zhen-hong2, SHEN Bing-hui2 |
1.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2.College of Science, China Agricultural University, Beijing 100083, China |
|
|
Abstract Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method(BP-ANN), and the numbers of principal components were calculated by the method of cross validation.The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat.The correlation coefficient (r) of calibration set was 0.960 4, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively.The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively.It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.
|
Received: 2009-01-26
Accepted: 2009-04-28
|
|
Corresponding Authors:
JI Hai-yan
E-mail: instru@cau.edu.cn
|
|
[1] XUE Li-hong, LUO Wei-hong, CAO Wei-xing, et al(薛利红,罗卫红,曹卫星,等).Journal of Remote Sensing(遥感学报), 2003, 7(1): 73. [2] GUO Jin-song, XU Fu-li, WANG Zhen(郭劲松, 徐福利, 王 振).Journal of Anhui Agri.Sci.(安徽农业科学), 2007, 35(21): 6407. [3] WANG Ji-hua, ZHAO Chun-jiang, GUO Xiao-wei(王纪华, 赵春江, 郭晓维).Scienuia Agricultura Sinica(中国农业科学), 2001, 34(1): 104. [4] Zhou Qifa, Shen Zhangquan, Wang Renchao.Acta Botanica Sinica, 2002, 44(5): 547. [5] MA Chao-fei, MA Jian-wen, HAN Xiu-zhen(马超飞, 马建文, 韩秀珍).Journal of Remote Sensinc(遥感学报), 2001, 5(5): 334. [6] Zhao Duli, Reddy K, Kakani V G, et al.European J.Agronomy, 2005, 22(4): 391. [7] WANG Xiu-zhen, WANG Ren-chao, LI Yun-mei(王秀珍, 王人潮, 李云梅).Journal of Zhejiang University(Agric.& Life Sci.)(浙江大学学报·农业与生命科学版), 2001, 27(3): 301. [8] SHEN Zhang-quan, WANG Ke, ZHU Jun-yan(沈掌泉, 王 珂, 朱君艳).Bulletin of Science and Technology(科技通报), 2002, 18(3): 174. [9] Minoltac L.ChlomphyⅡ SPAD-502 Instruction Manual[A].Radiometric Instruments Operations, 1989.17. [10] TANG Yan-lin, WANG Ren-chao, ZHANG Jin-heng, et al(唐延林, 王人潮, 张金恒, 等).Journal of Tritigeae Grops(麦类作物学报), 2003, 23(1): 63. [11] Turner F T, Jund M F.Agron Journal, 1991, 83: 926. [12] WU Fei-bo, XU Fu-hua, JIN Zhu-qun(邬飞波, 许馥华, 金珠群).Journal of Plant Physiology(作物学报),1999,25(40): 485. [13] ZHAO Xiang,LIU Su-hong,WANG Pei-juan,et al(赵 祥,刘素红,王培娟,等).Geography and Geo-Information Science(地理与地理信息科学),2004,20(3): 36. [14] LEI Hao-dong, MENG Yao-yong, LIAO Yu-bo, et al(雷浩东,孟耀勇,廖昱博,等).Acta Laser Biology Sinica(激光生物学报),2007,16(4): 490. [15] SHAO Yong-ni, HE Yong, BAO Yi-dan(邵咏妮,何 勇,鲍一丹).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2008,28(3): 602. [16] JI Hai-yan, YAN Yan-lu(吉海彦,严衍禄).Journal of Instrumental Analysis(分析测试学报),1999,18(3): 12. [17] LIANG Yi-zeng, YU Ru-qin(梁逸曾,俞汝勤).Chemometrics(化学计量学).Beijing: Chemical Industry Press(北京: 化学工业出版社),2000. [18] ZHU Da-qi(朱大奇).Journal of Southern Yangtze University(Natural Science Edition)(江南大学学报·自然科学版),2004,3(1): 103. [19] Stone, M L, Solie J B, Raun W R, et al.Transaction of the ASAE,1996, 39(5): 1623.
|
[1] |
ZHU Meng-yuan1, 2, LÜ Bin1, 2*, GUO Ying2. Comparison of Haematite and Goethite Contents in Aeolian Deposits in Different Climate Zones Based on Diffuse Reflectance Spectroscopy and Chromaticity Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1684-1690. |
[2] |
WENG Shi-zhuang*, CHU Zhao-jie, WANG Man-qin, WANG Nian. Reflectance Spectroscopy for Accurate and Fast Analysis of Saturated
Fatty Acid of Edible Oil Using Spectroscopy-Based 2D Convolution
Regression Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1490-1496. |
[3] |
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. |
[4] |
ZHANG Zhao1, 2, 3, 4, YAO Zhi-feng1, 3, 4, WANG Peng1, 3, 4, SU Bao-feng1, 3, 4, LIU Bin3, 4, 5, SONG Huai-bo1, 3, 4, HE Dong-jian1, 3, 4*, XU Yan5, 6, 7, HU Jing-bo2. Early Detection of Plasmopara Viticola Infection in Grapevine Leaves Using Chlorophyll Fluorescence Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1028-1035. |
[5] |
AN Ying1, 2, 4, DING Jing3, LIN Chao2, LIU Zhi-liang1, 4*. Inversion Method of Chlorophyll Concentration Based on
Relative Reflection Depths[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1083-1091. |
[6] |
YANG Xu, LU Xue-he, SHI Jing-ming, LI Jing, JU Wei-min*. Inversion of Rice Leaf Chlorophyll Content Based on Sentinel-2 Satellite Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 866-872. |
[7] |
DUAN Wei-na1, 2, JING Xia1*, LIU Liang-yun2, ZHANG Teng1, ZHANG Li-hua3. Monitoring of Wheat Stripe Rust Based on Integration of SIF and Reflectance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 859-865. |
[8] |
WANG Chun-juan1, 2, ZHOU Bin1, 2*, ZHENG Yao-yao3, YU Zhi-feng1, 2. Navigation Observation of Reflectance Spectrum of Water Surface in Inland Rivers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 878-883. |
[9] |
TANG Yu-zhe, HONG Mei, HAO Jia-yong, WANG Xu, ZHANG He-jing, ZHANG Wei-jian, LI Fei*. Estimation of Chlorophyll Content in Maize Leaves Based on Optimized Area Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 924-932. |
[10] |
JIANG Jie1, YU Quan-zhou1, 2, 3*, LIANG Tian-quan1, 2, TANG Qing-xin1, 2, 3, ZHANG Ying-hao1, 3, ZHANG Huai-zhen1, 2, 3. Analysis of Spectral Characteristics of Different Wetland Landscapes Based on EO-1 Hyperion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 517-523. |
[11] |
ZHU Li-wei, YAN Jin-xin, HUANG Juan, SHI Tao-xiong, CAI Fang, LI Hong-you, CHEN Qing-fu*, CHEN Qi-jiao*. Rapid Determination of Amino Acids in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy and Artificial Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 49-55. |
[12] |
ZHU Hai-jun1, FU Hong-yu1, 2, WANG Xue-hua1*, CUI Guo-xian1, 2*,SHI Ai-long1, XUE Wei-chun3. Preliminary Study on the Intertemporal Predictability of the Physiological Index of Early Rice Based on Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 170-175. |
[13] |
XU Zhao-jin, LI Dong-liang, SHEN Li*. Study on Diffuse Reflection and Absorption Spectra of Organic and Inorganic Chinese Painting Pigments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3915-3921. |
[14] |
LI Li-jie1,2, YUE Yan-bin2, WANG Yan-cang3, ZHAO Ze-ying2, LI Rui-jun2, NIE Ke-yan2, YUAN Ling1*. The Quantitative Study on Chlorophyll Content of Hylocereus polyrhizus Based on Hyperspectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3538-3544. |
[15] |
WANG Cong1, Mara Camaiti2, TIE Fu-de1,3, ZHAO Xi-chen4, CAO Yi-jian5*. Preliminary Study on the Non-Invasive Characterization of Organic Binding Media Employing a Portable Hyperspectral Sensor[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2886-2891. |
|
|
|
|