|
|
|
|
|
|
Detection of Rice Sheath Blight Disease Index Based on Split-Window Gram-Schmidt Transformation and PSO-SVR Algorithm |
XIAO Wen1, CAO Ying-li1,2*, FENG Shuai1, LIU Ya-di1, JIANG Kai-lun1, YU Zheng-xin1, YAN Li1 |
1. College of Information and Electrical Engineering,Shenyang Agricultural University, Shenyang 110161, China
2. Liaoning Agricultural Information Engineering Technology Center,Shenyang Agricultural University, Shenyang 110161, China |
|
|
Abstract Sheath blight is one of the main diseases of rice, whose control is of great significance to ensure rice yield and quality. Hyperspectral detection of rice diseases has been widely adopted in recent years, and hyperspectral dimensionality reduction is an important part of spectral analysis. In this study, the hyperspectral data of low altitude remote sensing canopy and rice ground canopy were obtained in Shenyang Agricultural University rice proving ground in 2019, and were smoothed by Savitzky-Golay with a window width of 15 and order of 3, as well as spectral transformations (original reflection spectrum, first-order differential reflection spectrum and inverse-log reflection spectrum), were carried out. To reduce the dimension of hyperspectral data in these 3 spectra, the split-window Gram-Schmidt transform method was used to find the projection space and map the main substrate, in which the main base with significant probability was drawn, and its maximum and minimum value was the characteristic band. The principal component analysis and successive projections algorithm were also used for dimensionality reduction of three spectra. Dimension-reduced data and rice sheath blight disease index were modeled by support vector machine regression, which was used for particle swarm optimization and radial basis function as the kernel function. The effect of three-dimensionality reduction methods was compared and analyzed. The results showed that the modeling effect of the rice ground canopy scale was better than that of the low-altitude remote sensing scale; in the aspect of hyperspectral data processing, the inverse logarithm transformation effect of low-altitude canopy hyperspectral data was better, and the first-order differential transformation effect of ground canopy hyperspectral data was better; the split-window Gram-Schmidt transformation algorithm was better than principal component analysis and successive projections algorithm; particle swarm optimization could optimize the penalty coefficient and kernel function parameters in SVR, and improve the inversion accuracy; in the low-altitude remote sensing canopy scale, the hyperspectral spectrum was processed by using the inverse logarithm processing and the split-window Gram-Schmidt transform, whose sensitive bands were 427.3, 539.6, 749.5 and 825.4 nm respectively. The determination coefficient R2 was 0.731 and RMSE was 0.151 by using the PSO-SVR model; in the ground canopy scale, the hyperspectral spectrum was processed by using the first order differential processing and the split-window Gram-Schmidt transform, whose sensitive bands were 552, 607, 702 and 730 nm respectively. The determination coefficient R2 was 0.778 and RMSE was 0.147 by using the PSO-SVR model. In conclusion, rice sheath blight can be effectively detected by hyperspectral technology, and its disease index can be retrieved by canopy hyperspectral analysis. The split-window Gram-Schmidt transform has a good effect on the dimensionality reduction of hyperspectral data. PSO-SVR modeling can significantly improve the inversion of rice sheath blight disease index. The results can provide a theoretical basis and technical support for the detection of rice sheath blight and disease occurrence on the canopy scale.
|
Received: 2020-07-02
Accepted: 2020-11-22
|
|
Corresponding Authors:
CAO Ying-li
E-mail: caoyingli@163.com
|
|
[1] ZHANG Jing-cheng, YUAN Lin, WANG Ji-hua, et al(张竞成, 袁 琳, 王纪华, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(20): 1.
[2] Mohsen Azadbakht, Davoud Ashourloo, Hossein Aghighi, et al. Computers and Electronics in Agriculture, 2019, 156: 119.
[3] Alireza Pourreza, Won S Lee, Mark A Ritenour, et al. Horttechnology, 2016, 26(3): 254.
[4] Faranak Ghobadifar, Aimrun Wayayok, Shattri Mansor, et al. Geomatics Natural Hazards & Risk, 2016, 7(1): 237.
[5] Zhang Dongyan, Zhou Xingen, Zhang Jian, et al. PLOS ONE, 2018, 13(5): 0187470.
[6] ZHAO Xiao-yang, ZHANG Jian, ZHANG Dong-yan, et al(赵晓阳, 张 建, 张东彦, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(4): 1192.
[7] Deng Xiaoling, Lan Yubin, Hong Tiansheng, et al. Computers and Electronics in Agriculture, 2016, 130: 177.
[8] LAN Yu-bin, ZHU Zi-hao, DENG Xiao-ling, et al(兰玉彬, 朱梓豪, 邓小玲, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(3): 92. |
[1] |
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. |
[2] |
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. |
[3] |
XU Su-an, WANG Jia-xiang, LIU Yong. Detection of Adulteration of Vine Pepper Oil by Near-Infrared
Spectroscopy Combined With Improved Whale Optimization
Algorithm Model BAS-WOA-SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 569-576. |
[4] |
ZHANG Zhe-yu, LI Yao-xiang*, WANG Zhi-yuan, LI Chun-xu. NIR Model Optimization Study of Larch Wood Density Based on IFSR Abnormal Sample Elimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3395-3402. |
[5] |
SONG Ni-na1, XIAO Dong1*, LI Sen1, GAO Yu-jie2. Analysis of Soil Salinity Based on Spectrum and RVIPSO-MELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2482-2487. |
[6] |
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. |
[7] |
LI Ming-liang1, DAI Yu-jia1, QIN Shuang1, SONG Chao2*, GAO Xun1*, LIN Jing-quan1. Influence of LIBS Analysis Model on Quantitative Analysis Precision of Aluminum Alloy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 587-591. |
[8] |
LIN Xiao-mei1, WANG Xiao-meng1, HUANG Yu-tao1*, LIN Jing-jun2*. PSO-LSSVM Improves the Accuracy of LIBS Quantitative Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3583-3587. |
[9] |
MA Li1, 2, FAN Xin-li1, 2, ZHANG Shuo1, 2, WANG Wei-feng1, 2, WEI Gao-ming1, 2. Research on CH4 Gas Detection and Temperature Correction Based on TDLAS Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3632-3638. |
[10] |
YAN Peng-cheng1, 2, SHANG Song-hang2*, ZHANG Chao-yin2, ZHANG Xiao-fei2. Classification of Coal Mine Water Sources by Improved BP Neural Network Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2288-2293. |
[11] |
ZHANG Zhao1, 2, 3, 4, WANG Peng1, 3, 4, YAO Zhi-feng1, 3, 4, QIN Li-feng1, 3, 4, HE Dong-jian1, 3, 4*, XU Yan5, 6, ZHANG Jian-xia5, 6, HU Jing-bo2. Early Detection of Downy Mildew on Grape Leaves Using Multicolor Fluorescence Imaging and Model SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 828-834. |
[12] |
FU Xing-hu, ZHAO Fei, WANG Zhen-xing, LU Xin, FU Guang-wei, JIN Wa, BI Wei-hong. Quantitative Analysis of Goat Serum Protein Content by Raman Spectroscopy Based on IABC-SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 540-545. |
[13] |
CHEN Bei1, ZHENG En-rang1*, MA Jin-fang2, GE Fa-huan3, XIAO Huan-xian4. Prediction Method for Production Year of Antai Pills Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2592-2597. |
[14] |
BAI Xue-bing, YU Jian-shu, FU Ze-tian, ZHANG Ling-xian, LI Xin-xing*. Application of Spectral Imaging Technology for Detecting Crop Disease Information: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 350-355. |
[15] |
MU Yong-huan1, QIU Bo1*, WEI Shi-ya1, SONG Tao1, ZHENG Zi-peng1, GUO Ping2*. Regression Prediction of Photometric Redshift Based on Particle Warm Optimization Neural Network Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2693-2697. |
|
|
|
|