|
|
|
|
|
|
Classification of Impurities in Machine-Harvested Seed Cotton Using Hyperspectral Imaging |
CHANG Jin-qiang, ZHANG Ruo-yu*, PANG Yu-jie, ZHANG Meng-yun, ZHA Ya |
College of Mechanical and Electrical Engineering, Shihezi University/Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture, Shihezi 832003,China |
|
|
Abstract The classification and detection of impurities in machine-harvested seed cotton provides a reference for adjusting cotton cleaning mechanical processing parameters and has important significance for improving lint quality. However, the uneven distribution of seed cotton makes image detection more difficult, and traditional detection methods cannot effectively detect various impurities. The hyperspectral imaging method was used to discriminate the five impurities (cotton leaf, cotton stem, plastic film, hull inner, and hull outer) in the machine-harvested seed cotton. The hyperspectral images of 120 machine-harvested seed cotton samples were collected, and the region of interest was selected to obtain the average spectral curve. Due to the difference in the composition of materials, various impurities showed different spectral absorption and reflection characteristics, and the spectral difference of the characteristics of different materials was greater than that of similar materials. Principal component analysis (PCA) of the extracted average spectral curve showed that cotton, plastic film and hull outer were better separable than the other three types. However, the spectral distributions of cotton leaf, hull inner, and cotton stem overlapped seriously. Based on the extracted average spectral curve as the training sample, three discrimination algorithms of linear discriminant analysis (LDA), support vector machine (SVM) and neural network (ANN) were used to optimize the algorithm parameters and finally established the impurity detection model. Among them, the sample space after dimensionality reduction of the LDA model shows better separability than PCA. Regularization was used to prevent overfitting in LDA, and the accuracy rate of the training set was 86.4%, and the accuracy of the test set was 86.2%. The parameter optimization result of the SVM model was C=105, g=0.1. The accuracy of the training set was 83.42%, and the accuracy of the test set was 83.40%. The parameter optimization result of the ANN model was that the number of hidden layers and neurons were 1 and 10, respectively. The accuracy rate of the training set was 82.9%, and the accuracy rate of the test set was 81.8%. Comparing the classification accuracy and detection time of the three models, the results of the LDA model were all the best. Through the pixel level discrimination of hyperspectral images, the results show that both cotton and botanical impurities were effectively detected. However, there were misidentifications between plastic film and cotton, which was consistent with the results of the impurity spectrum classification discrimination model. Therefore, hyperspectral imaging technology can detect and identify seed cotton impurities quickly and non-destructively and provide feedback parameters for cotton processing equipment, which is of great significance to the mechanization and intelligence of cotton processing.
|
Received: 2020-08-28
Accepted: 2021-01-11
|
|
Corresponding Authors:
ZHANG Ruo-yu
E-mail: ry248@163.com
|
|
[1] WANG Xin, LI Dao-liang, YANG Wen-zhu, et al(王 欣, 李道亮, 杨文柱, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(8): 7.
[2] Zhou W H, Xu S D, Liu C J, et al. Applied Spectroscopy Reviews, 2016, 51(4): 298.
[3] ZHANG Zhi-feng, ZHAI Yu-sheng, GUO Ying-ying, et al(张志峰,翟玉生, 郭莹莹,等). Laser & Optoelectronics Progress(激光与光电子学进展), 2015, 52(3): 154.
[4] ZHANG Lin, WEI Ping, WU Jian-bo, et al(张 林, 韦 平, 伍剑波, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(15): 289.
[5] ZHANG Cheng-liang, LI Lei, DONG Quan-cheng, et al(张成梁, 李 蕾, 董全成, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(24): 189.
[6] ZHANG Cheng-liang, LI Lei, DONG Quan-cheng, et al(张成梁, 李 蕾, 董全成, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2016, 47(7): 28.
[7] WANG Hao-peng, LI Hui(王昊鹏, 李 慧). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(3): 236.
[8] Ni Chao, Li Zhenye, Zhang Xiong, et al. IEEE Access, 2020, 8: 93028.
[9] Wang Xin, Yang Wenzhu, Li Zhenbo. Computers and Electrical Engineering, 2015, 46: 500.
[10] Fortier C, Cintron M S, Rodgers J. AATCC Journal of Research, 2015, 2(6): 1.
[11] Jiang Yu, Li Changying. PLOS ONE, 2015, 10(3): e0121969.
[12] Jiang Y, Li C. Computers and Electronics in Agriculture, 2015, 119: 191.
[13] Mustafic A, Li C. Textile Research Journal, 2015, 85(12): 1209.
[14] Zhang R Y, Li C Y, Zhang M Y, et al. Computers and Electronics in Agriculture, 2016, 127: 260.
[15] Zhang M Y, Li C Y, Yang F Z. Computers and Electronics in Agriculture, 2017, 139: 75. |
[1] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[2] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[3] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[4] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[5] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[6] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
[7] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
[8] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
[9] |
LIU Gang1, LÜ Jia-ming1, NIU Wen-xing1, LI Qi-feng2, ZHANG Ying-hu2, YANG Yun-peng2, MA Xiang-yun2*. Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1697-1702. |
[10] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
[11] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
[12] |
ZHANG Fan1, WANG Wen-xiu1, ZHANG Yu-fan1, HU Ze-xuan1, ZHAO Dan-yang1, MA Qian-yun1, SHI Hai-yan2, SUN Jian-feng1*. Hyperspectral and Ensemble Learning Method for Rapid Identification of Black Spot in Yali Pear at Gley Stage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1541-1549. |
[13] |
GUO Feng1, ZHAO Dong-e1*, YANG Xue-feng1, CHU Wen-bo2, ZHANG Bin1, ZHANG Da-shun3MENG Fan-jun3. Research on Hyperspectral Image Recognition of Iron Fragments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 997-1003. |
[14] |
JIA Meng-meng, YIN Yong*, YU Hui-chun, YUAN Yun-xia, WANG Zhi-hao. Hyperspectral Imaging Combined With Feature Wavelength Screening for Monitoring the Quality Change of Tomato During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 969-975. |
[15] |
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. |
|
|
|
|