| 光谱学与光谱分析 |
|
|
|
|
|
| Prediction Model of Net Photosynthetic Rate of Ginseng under Forest Based on Optimized Parameters Support Vector Machine |
| WU Hai-wei1, 2，YU Hai-ye1*，ZHANG Lei1 |
1. Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130022, China
2. School of Electrical and Information Engineering, Beihua University, Jilin 132021, China |
|
|
|
|
Abstract Using K-fold cross validation method and two support vector machine functions, four kernel functions, grid-search, genetic algorithm and particle swarm optimization, the authors constructed the support vector machine model of the best penalty parameter c and the best correlation coefficient. Using information granulation technology, the authors constructed P particle and ε particle about those factors affecting net photosynthetic rate, and reduced these dimensions of the determinant. P particle includes the percent of visible spectrum ingredients. ε particle includes leaf temperature, scattering radiation, air temperature, and so on. It is possible to obtain the best correlation coefficient among photosynthetic effective radiation, visible spectrum and individual net photosynthetic rate by this technology. The authors constructed the training set and the forecasting set including photosynthetic effective radiation, P particle and ε particle. The result shows that epsilon-SVR-RBF-genetic algorithm model, nu-SVR-linear-grid-search model and nu-SVR-RBF-genetic algorithm model obtain the correlation coefficient of up to 97% about the forecasting set including photosynthetic effective radiation and P particle. The penalty parameter c of nu-SVR-linear-grid-search model is the minimum, so the model’s generalization ability is the best. The authors forecasted the forecasting set including photosynthetic effective radiation, P particle and ε particle by the model, and the correlation coefficient is up to 96%.
|
|
Received: 2010-09-26
Accepted: 2010-12-20
|
|
|
|
Corresponding Authors:
YU Hai-ye
E-mail: haiye@jlu.edu.cn
|
|
[1] XIN Zhi-yun, GU Ming(辛治运，顾 明). Journal of Tsinghua University(Sci. & Tech.)(清华大学学报·自然科学版), 2008, 48(7): 1147.
[2] Manevitz L M, Yousef M. Journal of Machine Learning Research, 2001, 2: 139.
[3] HUANG Qian, WANG Zhen, WEI Tao, et al(黄 谦，王 震，韦 韬，等). Computer Engineering(计算机工程), 2006, 32(16): 127.
[4] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer Press, 1995.
[5] Evqeniou T, Pontil M. Machine Learing and Its Application. Advanced Lectures, Springer Publisher, 2005. 249.
[6] YU Ke，CHENG Yi-yu(虞 科，程翼宇). Chinese Journal of Analytical Chemistry(分析化学)，2006, 34(4): 561.
[7] HOU Zhen-yu, CAI Wen-sheng, SHAO Xue-guang(侯振雨，蔡文生，邵学广). Chinese Journal of Analytical Chemistry(分析化学)，2006, 34(5): 617.
[8] YANG Jian-lei, ZHU Tuo, XU Yan, et al(杨建磊，朱 拓，徐 岩，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2010, 30(1): 243.
[9] WU Haiwei, YU Haiye, YANG Haoyu, et al. 2010 International Conference on Machine Vision and Human-Machine Interface，2010. 109.
[10] YU Hai-ye，ZHANG Lei，ZHOU Li-na，et al(于海业，张 蕾，周丽娜，等). Journal of Jilin Agricultural University(吉林农业大学学报), 2007, 29(3): 237.
|
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
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. |
| [4] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [5] |
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. |
| [6] |
CHENG Gang1, CAO Ya-nan1, TIAN Xing1, CAO Yuan2, LIU Kun2. Simulation of Airflow Performance and Parameter Optimization of
Photoacoustic Cell Based on Orthogonal Test[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3899-3905. |
| [7] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [8] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
| [9] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
| [10] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [11] |
LÜ Shi-lei1, 2, 3, WANG Hong-wei1, LI Zhen1, 2, 3*, ZHOU Xu1, ZHAO Jing1. Hyperspectral Identification Model of Cantonese Tangerine Peel Based on BWO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2894-2901. |
| [12] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
| [13] |
CHEN Chao-yang1, 2, LIU Cui-hong1, 2, LI Zhi-bin3, Andy Hsitien Shen1, 2*. Alexandrite Effect Origin of Gem Grade Diaspore[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2557-2562. |
| [14] |
LI Shu-fei1, LI Kai-yu1, QIAO Yan2, ZHANG Ling-xian1*. Cucumber Disease Detection Method Based on Visible Light Spectrum and Improved YOLOv5 in Natural Scenes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2596-2600. |
| [15] |
ZHANG Jing, GUO Zhen, WANG Si-hua, YUE Ming-hui, ZHANG Shan-shan, PENG Hui-hui, YIN Xiang, DU Juan*, MA Cheng-ye*. Comparison of Methods for Water Content in Rice by Portable Near-Infrared and Visible Light Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2059-2066. |
|
|
|
|
