|
|
|
|
|
|
| Research on Anti-Moisture Interference Soil Organic Matter ModelBased on Characteristic Wavelength Integration Algorithm |
| ZHAO Rui1, SONG Hai-yan1*, ZHAO Yao2, SU Qin1, LI Wei1, SUN Yi-shu1, CHEN Ying-min1 |
1. College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030800, China
2. College of Horticulture, Shanxi Agricultural University, Taigu 030800, China |
|
|
|
|
Abstract As an important component in soil, soil organic matter (SOM) is a critical nutrition index in the process of crop growth. Rapid and accurate detection of SOM content is of great significance for the fertilization management. In recent years, NIR has been widely used in the rapid detection of SOM. However, soil moisture is one of the important factors that affect the prediction results of SOM. In this study, 140 soil samples were collected in Shanxi Province, and the spectral information with different water content (0%, 5%, 10%, 15%, 17%) was collected by ASD spectrometer (350~2 500 nm). In order to improve the accuracy of the SOM prediction model, a characteristic wavelength integration algorithm (taking the integral absorbance value at characteristic wavelength as the independent variable) was proposed. The results show that: (1) the statistical parameters of the SOM prediction model established by this algorithm are better than the traditional characteristic wavelength modeling method; (2) the moisture correction model established by this algorithm can eliminate the influence of moisture, and the corrected spectra of wet soil samples are closer to the corresponding dry soil samples; (3) the prediction accuracy of wet soil samples is improved. The RP increased by about 0.09 and RMSEP decreased by about 1.72. The results show that the method can effectively reduce the influence of soil moisture on the spectral characteristics of SOM, improve the prediction accuracy of SOM with different water content, and provide theoretical support for the subsequent instrument development.
|
|
Received: 2021-06-28
Accepted: 2021-11-25
|
|
|
|
Corresponding Authors:
SONG Hai-yan
E-mail: haiyansong2003@163.com
|
|
[1] Chu Xiaoli. Practical Handbook of Near Infrared Spectroscopy. China Machine Press，2016.
[2] Zhen Lihua, Li Minzan, An Xiaofei，et al. Transactions of the Chinese Society of Agricultural Engineering, 2016, 26(S2): 81.
[3] Tian Yongchao, Zhang Juanjuan, Yao Xia，et al. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(1): 145.
[4] Li Guanwen, Gao Xiaohong, Yang Lingyu, et al. Soil Bulletin, 2017, 48(6): 1360.
[5] Yu Lei, Hong Yongsheng, Zhou Yong, et al. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(13): 95.
[6] Qin Huajun, Liu Boping, Luo Xiang, et al. Advanced Measurement and Laboratory Management, 2007, (1): 23.
[7] Roberts J J, Cozzolino D. NIR News, 2017, 28(4): 3.
[8] Hong Yongsheng, Yu Lei, Zhu Yaxing, et al. Acta Pedologica Sinica, 2017, (5): 1068.
[9] Wang C K，Pan X Z. Journal of Near Infrared Spectroscopy，2016，24(3)：231.
[10] Liu Ya, Pan Xianzhang, Wang Changkun, et al. PLOS ONE, 2015, 10(10).
[11] WANG Shi-fang, HAN Ping, SONG Hai-yan, et al. Spectroscopy and Spectral Analysis, 2019, 39(6): 1986.
[12] Zhang Lei, Zhang Rongbiao. Polish Journal of Environmental, 2017, 26(1): 395.
[13] Wei Guangfei, Li Yu, Zhang Zhitao, et al. Peerj, 2020, 8: e9087.
[14] Knox N M, Grunwald S, McDowell M L, et al. Geoderma, 2015, 239: 229.
[15] Viscarra Rossel R A, Behrens T. Geoderma, 2010, 158(1-2): 46.
[16] ZHAO Wei, BAO Ni-sha, LIU Shan-jun, et al. Spectroscopy and Spectral Analysis, 2020, 40(7): 2188.
[17] HU Xiao-yan, CUI Xu, HAN Xiao-ping, et al. Spectroscopy and Spectral Analysis, 2019, 39(4): 1059.
|
| [1] |
SHI Wen-qiang1, XU Xiu-ying1*, ZHANG Wei1, ZHANG Ping2, SUN Hai-tian1, 3, HU Jun1. Prediction Model of Soil Moisture Content in Northern Cold Region Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1704-1710. |
| [2] |
WANG Xue-pei1, 2, ZHANG Lu-wei1, 2, BAI Xue-bing3, MO Xian-bin1, ZHANG Xiao-shuan1, 2*. Infrared Spectral Characterization of Ultraviolet Ozone Treatment on Substrate Surface for Flexible Electronics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1867-1873. |
| [3] |
WANG Yue1, 3, 4, CHEN Nan1, 2, 3, 4, WANG Bo-yu1, 5, LIU Tao1, 3, 4*, XIA Yang1, 2, 3, 4*. Fourier Transform Near-Infrared Spectral System Based on Laser-Driven Plasma Light Source[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1666-1673. |
| [4] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
| [5] |
YU Zhi-rong, HONG Ming-jian*. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1735-1740. |
| [6] |
MENG Fan-jia1, LUO Shi1, WU Yue-feng1, SUN Hong1, LIU Fei2, LI Min-zan1*, HUANG Wei3, LI Mu3. Characteristic Extraction Method and Discriminant Model of Ear Rot of Maize Seed Base on NIR Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1716-1720. |
| [7] |
PENG Yan-fang1, WANG Jun1, WU Zhi-sheng2*, LIU Xiao-na3, QIAO Yan-jiang2*. NIR Band Assignment of Tanshinone ⅡA and Cryptotanshinone by
2D-COS Technology and Model Application Tanshinone Extract[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1781-1785. |
| [8] |
WANG Li-qi1, YAO Jing1, WANG Rui-ying1, CHEN Ying-shu1, LUO Shu-nian2, WANG Wei-ning2, ZHANG Yan-rong1*. Research on Detection of Soybean Meal Quality by NIR Based on
PLS-GRNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1433-1438. |
| [9] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
| [10] |
LI Jia-yi1, YU Mei1, LI Mai-quan1, ZHENG Yu2*, LI Pao1, 3*. Nondestructive Identification of Different Chrysanthemum Varieties Based on Near-Infrared Spectroscopy and Pattern Recognition Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1129-1133. |
| [11] |
CHEN Chu-han1, ZHONG Yang-sheng2, WANG Xian-yan3, ZHAO Yi-kun1, DAI Fen1*. Feature Selection Algorithm for Identification of Male and Female
Cocoons Based on SVM Bootstrapping Re-Weighted Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1173-1178. |
| [12] |
LI Xue-ying1, 2, LI Zong-min3*, CHEN Guang-yuan4, QIU Hui-min2, HOU Guang-li2, FAN Ping-ping2*. Prediction of Tidal Flat Sediment Moisture Content Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1156-1161. |
| [13] |
ZHANG Xiao-hong1, JIANG Xue-song1*, SHEN Fei2*, JIANG Hong-zhe1, ZHOU Hong-ping1, HE Xue-ming2, JIANG Dian-cheng1, ZHANG Yi3. Design of Portable Flour Quality Safety Detector Based on Diffuse
Transmission Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1235-1242. |
| [14] |
Yumiti Maiming1, WANG Xue-mei1, 2*. Hyperspectral Estimation of Soil Organic Matter Content Based on Continuous Wavelet Transformation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1278-1284. |
| [15] |
ZHENG Kai-yi1, ZHANG Wen1, DING Fu-yuan1, ZHOU Chen-guang1, SHI Ji-yong1, Yoshinori Marunaka2, ZOU Xiao-bo1*. Using Ensemble Refinement (ER) Method to OptimizeTransfer Set of Near-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1323-1328. |
|
|
|
|
