光谱学与光谱分析 |
|
|
|
|
|
Research on Modeling Method for Chlorophyll Content Fine Measurement Based on Neural Network |
ZHOU Chun-yan1, 2, HUA Deng-xin1*, LE Jing1, WAN Wen-bo1, JIANG Peng1,MAO Jian-dong2 |
1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China 2. School of Electrical and Information Engineering, North University of Nationalities, Yinchuan 750021, China |
|
|
Abstract Aiming at SPAD values of living plant leaf chlorophyll content affected easily by the blade thickness, water content, etc, a fine retrieval method of chlorophyll content based on multiple parameters of neural network model is presented. The SPAD values and water index(WI) of leaves were obtained by the leaf transmittance under the irradiation of light central wavelength in 650nm, 940nm, 1450nm respectively. Meanwhile, the corresponding blade thickness is got by micrometer and the chlorophyll content is measured by spectrophotometric method. To modeling samples, the single parameter model between SPAD values and chlorophyll content was built and the nonlinear model between WI, thickness, SPAD values and chlorophyll content was established based on BP neural network. The predicted value of chlorophyll content of test samples were calculated separately by two models, and the correlation and relative errors were analyzed between predicted values and actual values. 340 samples of three different plant leaves were tested by the method described above in experiment. The results showed that compared with single parameter model, the prediction accuracy of three different plant samples were improved in different degrees, the average absolute relative error of chlorophyll content of all pooled samples predicted by BP neural network model reduced from 7.55% to 5.22%. the fitting determination coefficient is increased from 0.83 to 0.93. The feasibility were verified in this paper that the prediction accuracy of living plant chlorophyll content can improved effectively using multiple parameter BP neural network model.
|
Received: 2014-10-13
Accepted: 2015-01-20
|
|
Corresponding Authors:
HUA Deng-xin
E-mail: xauthdx@163.com
|
|
[1] Schlemmer M R, Francis D D, Shanahan J F, et al. Agron. J., 2005, 97: 106. [2] XIE Chuan-qi, HE Yong, LI Xiao-li, et al(谢传奇,何 勇,李晓丽,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(12): 3324. [3] WAN Wen-bo, HUA Deng-xin, LE Jing, et al(万文博,华灯鑫,乐 静,等). Acta Phys. Sin.(物理学报), 2013, 62(19): 190601. [4] Turner F T, Jund M F. Australian Journal of Experimental Agriculture, 1994, 34: 1001. [5] Fanizza G, Dellagatta C, Bagnulo C. Annals of Applied Biology, 1991, 119: 203. [6] John Markwell, John C Osterman,Jennifer L Mitchell. Photosynthesis Research, 1995, 46: 467. [7] Marenco R A, Antezana-Vera S A, Nascimento H S. Photosynthetica, 2009, 47(2): 184. [8] Dana E M, Juan J G. Agronomie, 2004, 24: 41. [9] Peng Shaobing, Felipe V G, Rebecca C L, et al. Agronomy Journal, 1993, 85: 987. [10] FANG Bo, GUO Chong-chong, LI Jia-fu, et al(方 波,郭冲冲,李家福,等). Ecological Economy(生态经济), 2003,(9): 137. [11] Thomas J R, Namken L N, Oerther G F, et al. Agronomy Journal, 1971, 63: 845. [12] Peuelas J, Filella I, Biel C, et al. International Journal of Remote Sensing, 1993, 14(10): 1887. |
[1] |
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. |
[2] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
[3] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
[4] |
ZHOU Qi1, 2, WANG Jian-jun1, 2*, HUO Zhong-yang1, 2*, LIU Chang1, 2, WANG Wei-ling1, 2, DING Lin3. UAV Multi-Spectral Remote Sensing Estimation of Wheat Canopy SPAD Value in Different Growth Periods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1912-1920. |
[5] |
ZHANG Fu1, 2, 3, CAO Wei-hua1, CUI Xia-hua1, WANG Xin-yue1, FU San-ling4*, ZHANG Ya-kun1. Non-Destructive Detection of Soluble Solids in Cherry Tomatoes by
Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 737-743. |
[6] |
JIA Meng-meng, YIN Yong*, YU Hui-chun, YUAN Yun-xia, WANG Zhi-hao. Hyperspectral Imaging Combined With Feature Wavelength Screening for Monitoring the Quality Change of Tomato During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 969-975. |
[7] |
WANG Yan-cang1, 4, LI Xiao-fang2, LI Li-jie5, LI Nan1, 4*, JIANG Qian-nan1, 4, GU Xiao-he3, YANG Xiu-feng1, 4LIN Jia-lu1, 4. Quantitative Inversion of Chlorophyll Content in Stem and Branch of
Pitaya Based on Discrete Wavelet Differential Transform Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 549-556. |
[8] |
LI Xiao-kai, YU Hai-ye, YU Yue, WANG Hong-jian, ZHANG Lei, ZHANG Xin, SUI Yuan-yuan*. Inversion Model of Clorophyll Content in Rice Based on a Bonic
Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 93-99. |
[9] |
FENG Hai-kuan1, 2, TAO Hui-lin1, ZHAO Yu1, YANG Fu-qin3, FAN Yi-guang1, YANG Gui-jun1*. Estimation of Chlorophyll Content in Winter Wheat Based on UAV Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3575-3580. |
[10] |
HU Xin-yu1, 2, XU Zhang-hua1, 2, 3, 5, 6*, HUANG Xu-ying1, 2, 8, ZHANG Yi-wei1, 2, CHEN Qiu-xia7, WANG Lin1, 2, LIU Hui4, LIU Zhi-cai1, 2. Relationship Between Chlorophyll and Leaf Spectral Characteristics and Their Changes Under the Stress of Phyllostachys Praecox[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2726-2739. |
[11] |
GUO Jing-jing1, YU Hai-ye1, LIU Shuang2, XIAO Fei1, ZHAO Xiao-man1, YANG Ya-ping1, TIAN Shao-nan1, ZHANG Lei1*. Study on the Hyperspectral Discrimination Method of Lettuce Leaf
Greenness[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2557-2564. |
[12] |
ZHANG Jiang1, CUI Jun-jie1, ZHENG Chang-song2*, LIU Yong1*, LIU Ya-jun3, SHEN Jian1. Stochastic Process Prediction of Clutch Remaining Life Based on Oil
Spectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2631-2636. |
[13] |
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. |
[14] |
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. |
[15] |
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. |
|
|
|
|