Laser Technology, Volume. 48, Issue 2, 281(2024)
Hyperspectral image detection of wheat seed purity based on SMOTE-UVE-SVM
[1] [1] BAO Y, MI C, WU N, et al. Rapid classification of wheat grain varieties using hyperspectral imaging and chemometrics[J]. Applied Sciences, 2019, 9(19): 4119.
[2] [2] FENG L, ZHU S, LIU F, et al. Hyperspectral imaging for seed quality and safety inspection: A review[J]. Plant Methods, 2019, 15(1): 1-25.
[3] [3] QIU Z, CHEN J, ZHAO Y, et al. Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network[J]. Applied Sciences, 2018, 8(2): 212.
[4] [4] YANG X, HONG H, YOU Z, et al. Spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification[J]. Sensors, 2015, 15(7): 15578-15594.
[5] [5] HUANG M,XIA Ch, ZHU Q B, et al. Recognizing wheat seed varieties using hyperspectral imaging technology combined with multi-scale 3D convolution neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(18): 153-160(in Chin-ese).
[6] [6] SINGH P, NAYYAR A, SINGH S, et al. Classification of wheat seeds using image processing and fuzzy clustered random forest[J]. International Journal of Agricultural Resources, Governance and Eco-logy, 2020, 16(2): 123-156.
[7] [7] WANG H Y, FAN H K, YAO Zh A, et al. Research of imbalanced data classification[J]. Application Research of Computers, 2008, 25(5): 1301-1304(in Chinese).
[8] [8] YAN H M, HE M Y. Hyperspectral data band selection based on clustering joint skewness-kurtosis index[J]. Journal of Signal Processing, 2023, 39(1): 1-10(in Chinese).
[9] [9] LU Y, REN Y, CUI B G. Noise robust band selection method for hyperspectral images[J]. National Remote Sensing Bulletin, 2022,26(11): 2382-2398(in Chinese).
[10] [10] YANG S, ZHU Q B, HUANG M. Application of joint skewness algorithm to select optimal wavelengths of hyperspectral image for maize seed classification [J]. Spectroscopy and Spectral Analysis, 2017, 37(3): 990-996.
[11] [11] LIU L, SHAO H, SUN L, et al. Detection of mildew and moisture content in timber by hyperspectral LiDAR[J]. Laser Technology, 2023, 47(5): 620-626(in Chinese).
[12] [12] HUANG M, HE C, ZHU Q, et al. Maize seed variety classification using the integration of spectral and image features combined with feature transformation based on hyperspectral imaging[J]. Applied Sciences, 2016, 6(6): 183.
[13] [13] BRUNING B, LIU H, BRIEN C, et al. The development of hyperspectral distribution maps to predict the content and distribution of nitrogen and water in wheat (Triticum aestivum)[J]. Frontiers in Plant Science, 2019, 10: 1380.
[14] [14] SINGH C B, JAYAS D S, PALIWAL J, et al. Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging[J]. Computers and Electronics in Agriculture, 2010, 73(2): 118-125.
[15] [15] TONG Y P, FENG W, SONG Y J, et al. Dynamic ensemble algorithm of SMOTE and rotation forest for imbalanced hyperspectral remote sensing classification[J]. National Remote Sensing Bulletin, 2022, 26(11): 2369-2381 (in Chinese).
[16] [16] DOU Z, GAO K, ZHANG X, et al. Band selection of hyperspectral images using attention-based autoencoders[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 18(1): 147-151.
[17] [17] BAJCSY P, GROVES P. Methodology for hyperspectral band selection[J]. Photogrammetric Engineering & Remote Sensing, 2004, 70(7): 793-802.
[18] [18] CENTNER V, MASSART D L, DE NOORD O E, et al. Elimination of uninformative variables for multivariate calibration[J]. Analytical Chemistry, 1996, 68(21): 3851-3858.
[19] [19] CORTES C, VAPNIK V. Support vector machine[J]. Machine Learning, 1995, 20(3): 273-297.
[20] [20] ZHANG Zh, LI Sh Q. Polarimetric SAR image classification based on AdaBoost improved random forest and SVM [J].Journal of University of Chinese Academy of Sciences, 2022, 39(6): 776-782 (in Chinese).
[21] [21] HUANG J, LI Y H, WU Sh B, et al. Research on driving style re-cognition based on multivariate feature parameters and an improved SVM algorithm[J]. Journal of Chongqing University of Technology(Natural Science Edition), 2022, 36(11): 8-19 (in Chinese).
[22] [22] YANG L, GAO M T. Experimental study about measurement of optical parameters of biological tissue based on least square support vector machine[J]. Laser Technology, 2015, 39(3): 300-303(in Chinese).
[23] [23] TAX D M J, DUIN R P W. Support vector data description[J]. Machine Learning, 2004, 54(1): 45-66.
[24] [24] KANG Y, ZHAO Zh H, WU H, et al. Deep SVDD-based anomaly detection method for communication signals[J]. Systems Engineering and Electronics, 2022, 44(7): 2319-2328 (in Chinese).
[25] [25] ZHAO Y, ZHANG X, SHANG Z, et al. A novel hybrid method for KPI anomaly detection based on VAE and SVDD[J]. Symmetry, 2021, 13(11): 2104.
[26] [26] JIANG W H, DUAN Y X, LI M Y, et al. A digital predistortion technique based on the dimension weighted blind K-nearest neighbor algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(2): 446-454 (in Chinese).
[27] [27] SYALIMAN K U. Enhance the accuracy of K-nearest neighbor (KNN) for unbalanced class data using synthetic minority oversampling technique (smote) and gain ratio (GR)[J]. INFOKUM, 2021, 10(1): 188-195.
[28] [28] DU J, LIU Zh G, YI Zh A. A KNN algorithm for unbalanced data set[J]. Science Technology and Engineering, 2011, 11(12): 2680-2685 (in Chinese).
[29] [29] ZHANG N N, ZHANG X, WANG Ch K, et al. Cotton LAI estimation based on hyperspectral and successive projection algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022,53(S1): 257-262 (in Chinese).
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ZHU Panyu, HUANG Min, ZHAO Xin. Hyperspectral image detection of wheat seed purity based on SMOTE-UVE-SVM[J]. Laser Technology, 2024, 48(2): 281
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Received: Mar. 2, 2023
Accepted: --
Published Online: Aug. 5, 2024
The Author Email: HUANG Min (huangmzqb@163.com)