|
|
|
|
|
|
Mechanism Improvement for Pretreatment Accuracy of Field Spectra of Saline Soil Using Fractional Differential Algorithm |
TIAN An-hong1, 2, XIONG Hei-gang3, 4*, ZHAO Jun-san1, FU Cheng-biao1, 2 |
1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. College of Information Engineering, Qujing Normal University, Qujing 655011, China
3. College of Applied Arts and Science, Beijing Union University, Beijing 100083, China
4. College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China |
|
|
Abstract Currently the application of fractional differential (FD) algorithm for field spectra and the optimal band mechanism of spectra inversion salt in saline soil has not been seen. In view of the problem that traditional integer-order differential algorithm causes fractional-order spectral information lose and model accuracy decrease the salinity and field spectra of saline soils in Fukang City of Xinjiang, China, were taken as data sources and original spectrum and common four transforms were subjected to a total of 21-order FDbetween 0~2 orders (interval is 0.1), in order to explore the mechanism improvement of pretreatment precision for saline soil spectra. Results showed that: (1) FD could accurately display the details of spectral transformation in the derivation process due to continuous orders, and improve the resolution between peaks of the spectra. Italso gradually changed peak shape and removed peaking operationdue to the increase of orders, which resulted in the gradual change of FD curve of saline soil to the slope of the curve, namely, a detailed description of subtle differences from 0-order to slope and between slope and curvature. (2) Correlation coefficient (CC) between FD value and salt content of five spectral transforms was tested by 0.01 significance level, and max CC absolute larger than integer order (1-order, 2-order) was mainly concentrated at 1.3, 1.4,1.5 orders. Among them, CC of 1.4 order 1/lgR and 1.3 order 1/R had the largest increase percentage, which were 12.78% and 13.03%, respectively. (3) Regardless of the spectral transformation, the corresponding bands for max CC between salt content and all FD value appeared at 598 nm (1/R) and 618 nm (R, ,1/lgR and lgR), and they were all in 1.3 or 1.4 order. (4) Na+ accounted for 65.74% of total cations, and its correlation with total salt was 0.738. The 589.3 nm spectrum was the main reason why the bands with the best correlation between various spectral transforms and soil salinity were located at 598 and 618 nm.
|
Received: 2018-07-03
Accepted: 2018-11-12
|
|
Corresponding Authors:
XIONG Hei-gang
E-mail: heigang@buu.edu.cn
|
|
[1] Mashimbye Z E, Cho M A, Nell J P, et al. Pedosphere, 2012, 22(5): 640.
[2] WANG Jing-zhe, TASHPOLAT Tiyip, ZHANG Dong(王敬哲, 塔西甫拉提·特依拜, 张 东). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(05): 152.
[3] CAO Lei, DING Jian-li, YU Hai-yang(曹 雷, 丁建丽, 于海洋). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(3): 101.
[4] ZHANG Dong, TASHPOLAT Tiyip, ZHANG Fei, et al(张 东, 塔西甫拉提·特依拜, 张 飞, 等). Acta Optica Sinica(光学学报), 2016, 36(3): 282.
[5] Zheng Kaiyi, Zhang Xuan, Tong Peijin, et al. Chinese Chemical Letters, 2015, 26: 293.
[6] GU Ji-hong, YU Yuan-jun, KANG De-hua, et al(顾继红, 于媛君, 亢德华, 等). Metallurgical Analysis(冶金分析), 2013, 33(10): 45.
[7] LI Si, BI Chao, DAI Ji-fu, et al(李 斯, 毕 超, 戴继福, 等). Physics Experimentation(物理实验),2015, 35(1): 42.
[8] WANG Jiao-liang, LONG Li-ping, XIE Dan(王姣亮, 龙立平, 谢 丹). Chinese Journal of Applied Chemistry(应用化学), 2016, 33(7): 841.
[9] LIU Zhi-wei, ZHANG Jun-wei, PENG Qi-jun(刘志维, 张军伟, 彭奇均). Food and Fermentation Industries(食品与发酵工艺), 2012, 38(9): 163. |
[1] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[2] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[3] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[4] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[5] |
PU Shan-shan, ZHENG En-rang*, CHEN Bei. Research on A Classification Algorithm of Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2446-2451. |
[6] |
JIN Cheng-liang1, WANG Yong-jun2*, HUANG He2, LIU Jun-min3. Application of High-Dimensional Infrared Spectral Data Preprocessing in the Origin Identification of Traditional Chinese Medicinal Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2238-2245. |
[7] |
LI Hao-dong1, 2, LI Ju-zi1*, CHEN Yan-lin1, HUANG Yu-jing1, Andy Hsitien Shen1*. Establishing Support Vector Machine SVM Recognition Model to Identify Jadeite Origin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2252-2257. |
[8] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
[9] |
HU Zheng1, ZHANG Yan1, 2*. Effect of Dimensionality Reduction and Noise Reduction on Hyperspectral Recognition During Incubation Period of Tomato Early Blight[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 744-752. |
[10] |
WANG Rui, SHI Lan-lan, WANG Yu-rong*. Rapid Prediction of Bending Properties of Catalpa Bungei Wood by
Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 557-562. |
[11] |
XIA Tong, LIU Yi-wei, GAO Yuan, CHENG Jie*, YIN Jian. Model-Fitting Methods for Mineral Raman Spectra Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 583-589. |
[12] |
CAI Yu1, 2, ZHAO Zhi-fang3, GUO Lian-bo4, CHEN Yun-zhong1, 2*, JIANG Qiong4, LIU Si-min1, 2, ZHANG Cong-zi4, KOU Wei-ping5, HU Xiu-juan5, DENG Fan6, HUANG Wei-hua7. Research on Origin Traceability of Rhizoma Dioscoreae Based on LIBS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 138-144. |
[13] |
WANG Jin-jie1, 2, 3, 4, 5, DING Jian-li1, 4, 5*, GE Xiang-yu1, 4, 5, ZHANG Zhe1, 4, 5, HAN Li-jing1, 4, 5. Application of Fractional Order Differential Technology in the Estimation of Soil Moisture Content Using UAV-Based Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3559-3567. |
[14] |
YAN Peng-cheng1, 2, ZHANG Xiao-fei2*, SHANG Song-hang2, ZHANG Chao-yin2. Research on Mine Water Inrush Identification Based on LIF and
LSTM Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3091-3096. |
[15] |
HUANG Hua1, NAN Meng-di1, LI Zheng-hao1, CHEN Qiu-ying1, LI Ting-jie1, GUO Jun-xian2*. Multi-Model Fusion Based on Fractional Differential Preprocessing and PCA-SRDA for the Origin Traceability of Red Fuji Apples[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3249-3255. |
|
|
|
|