|
|
|
|
|
|
Application of Joint Skewness Algorithm to Select Optimal Wavelengths of Hyperspectral Image for Maize Seed Classification |
YANG Sai, ZHU Qi-bing*, HUANG Min |
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China |
|
|
Abstract As an effective method for the nondestructive measurement of agricultural products quality, hyperspectral imaging technology has been widely studied in the field of seed classification and identification. Feature extraction and optimal wavelength selection are the two critical issues affecting the application of hyperspectral image in the field of seed identification. This study aimed to select optimal wavelengths from hyperspectral image data using joint skewness algorithm, so that they can be deployed in multispectral imaging-based inspection system for the automatic classification of maize seed. The hyperspectral images covering the wavelength range of 438~1 000 nm were acquired for 960 maize seeds including 10 varieties. After extracting the mean spectrum and entropy from the hyperspectral images, the joint skewness algorithm was used to select optimal wavelengths, and the classification models based on support vector machine were developed using the mean spectrum, entropy, and their combination, respectively. The experimental results indicated that the classification accuracy of the models developed by combination of the mean spectrum and entropy were higher than that of the mean spectrum or entropy for either full wavelengths or optimal wavelengths. The classification model for the combination of the mean spectrum and entropy based on the 10 optimal wavelengths selected by the joint skewness algorithm obtained 96.28% accuracy for test samples, with improvements of 4.30% and 20.38% over that of the mean spectrum and entropy, respectively, which was higher than the classification accuracy of the model that developed in the full wavelength (i.e., 93.47%). Meanwhile, the classification model based on joint skewness algorithm yielded the better classification accuracy than that of uninformative viable elimination algorithm, successive projections algorithm, and competitive adaptive reweighed sampling algorithm. This study made the online application of the hyperspectral image technology available for seed identification.
|
Received: 2016-02-26
Accepted: 2016-07-10
|
|
Corresponding Authors:
ZHU Qi-bing
E-mail: zhuqib@163.com
|
|
[1] Yang Yanli, Miao Changyun, Li Xianguo, et al. Optik, 2014, 125: 5803. [2] Cheng Shuxi, Sun Wenwen, Zhang Chu, et al. Spectroscopy and Spectral Analysis, 2014, 34(9): 2519. [3] Alireza P, Hamidreza P, Mohammad A, et al. Computers and Electronics in Agriculture, 2012, 83: 102. [4] Mohammed K, Sun Da-wen, Gamal E, et al. Talanta, 2013, 103: 130. [5] Teena M, Manickavasagan A, Ravikanth L, et al. Journal of Stored Products Research, 2014, 59: 306. [6] Ramón M, Francesco C, Amaury L, et al. Neurocomputing, 2014, 128: 207. [7] Tan Kezhu, Chai Yuhua, Song Weixian, et al. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30: 235. [8] Wang Lu, Liu Dan, Pu Hongbin, et al. Food Analytical Methods, 2015, 8: 515. [9] Hu Menghan, Dong QingLi, Liu BaoLin. Postharvest Biology & Technology, 2016, 115: 122. [10] Adrianode A G, Agustina V S, Héctor C G, et al. Anal. Bioanal. Chem., 2015, 407: 5649. [11] Fan Shuxiang, Zhang Baohua, Li Jiangbo, et al. Biosystems Engineering, 2016, 143: 9. [12] Huang Min, Zhao Weiyan, Wang Qingguo, et al. International Agrophysics, 2015, 29: 39. [13] Huang Min, Ma Yanan, Li Yanhua, et al. Analytical Methods, 2014, 6: 7793. [14] Geng Xiurui, Ji Luyan, Sun Kang. IEEE Geoscience and Remote Sensing Letters, 2014, 11: 1821. [15] Geng Xiurui, Ji Luyan, Zhao Yongchao, et al. IEEE Geosci and Remote Sens. Lett., 2013, 10: 1305. [16] Yang Xiaoling, Hong Hanmei, You Zhaohong, et al. Sensors, 2015, 15: 15578. [17] Zhu Qibing, Feng Zhaoli, Huang Min, et al. Transactions of the Chinese Society of Agricultural Engineering, 2012, 23: 271. |
[1] |
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. |
[2] |
LI Quan-lun1, CHEN Zheng-guang1*, SUN Xian-da2. Rapid Detection of Total Organic Carbon in Oil Shale Based on Near
Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1691-1697. |
[3] |
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. |
[4] |
DAI Ruo-chen1, TANG Huan2*, TANG Bin1*, ZHAO Ming-fu1, DAI Li-yong1, ZHAO Ya3, LONG Zou-rong1, ZHONG Nian-bing1. Study on Detection Method of Foxing on Paper Artifacts Based on
Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1567-1571. |
[5] |
LIU Mei-chen, XUE He-ru*, LIU Jiang-ping, DAI Rong-rong, HU Peng-wei, HUANG Qing, JIANG Xin-hua. Hyperspectral Analysis of Milk Protein Content Using SVM Optimized by Sparrow Search Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1601-1606. |
[6] |
ZHANG Tian-liang, ZHANG Dong-xing, CUI Tao, YANG Li*, XIE Chun-ji, DU Zhao-hui, ZHONG Xiang-jun. Identification of Early Lodging Resistance of Maize by Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1229-1234. |
[7] |
KONG Yu-ru1, 2, WANG Li-juan1*, FENG Hai-kuan2, XU Yi1, LIANG Liang1, XU Lu1, YANG Xiao-dong2*, ZHANG Qing-qi1. Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 933-939. |
[8] |
HUI Yun-ting1, WANG De-cheng1, TANG Xin2, PENG Yao-qi1, WANG Hong-da1, ZHANG Hai-feng1, YOU Yong1*. Detection of Sorghum-Sudan Grass Seed Germination Rate Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 423-427. |
[9] |
JIANG Jie1, YU Quan-zhou1, 2, 3*, LIANG Tian-quan1, 2, TANG Qing-xin1, 2, 3, ZHANG Ying-hao1, 3, ZHANG Huai-zhen1, 2, 3. Analysis of Spectral Characteristics of Different Wetland Landscapes Based on EO-1 Hyperion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 517-523. |
[10] |
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. |
[11] |
QIN Shuang1, LI Ming-liang1, DAI Yu-jia1, GAO Xun1*, SONG Chao2*, LIN Jing-quan1. The Accuracy Improvement of Fe Element in Aluminum Alloy by Millisecond Laser Induced Breakdown Spectroscopy Under Spatial Confinement Combined With Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 582-586. |
[12] |
CAO Qiu-hong, LIN Hong-mei, ZHOU Wei, LI Zhao-xin, ZHANG Tong-jun, HUANG Hai-qing, LI Xue-min, LI De-hua*. Water Quality Analysis Based on Terahertz Attenuated Total Reflection Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 31-37. |
[13] |
WU Ye-lan1, CHEN Yi-yu1, LIAN Xiao-qin1, LIAO Yu2, GAO Chao1, GUAN Hui-ning1, YU Chong-chong1. Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3837-3843. |
[14] |
ZHOU Bing, LI Bing-xuan*, HE Xuan, LIU He-xiong,WANG Fa-zhen. Classification of Camouflages Using Hyperspectral Images Combined With Fusing Adaptive Sparse Representation and Correlation Coefficient[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3851-3856. |
[15] |
LIN Hong-mei1, CAO Qiu-hong1, ZHANG Tong-jun1, LI Zhao-xin1, HUANG Hai-qing1, LI Xue-min1, WU Bin2, ZHANG Qing-jian3, LÜ Xin-min4, LI De-hua1*. Identification of Nephrite and Imitations Based on Terahertz Time-Domain Spectroscopy and Pattern Recognition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3352-3356. |
|
|
|
|