|
|
|
|
|
|
Study on Multi-Feature Model Fusion Variety Classification and Multi-Parameter Appearance Inspection for Milled Rice |
YANG Sen1, ZHANG Xin-ao1, XING Jian1, DAI Jing-min2 |
1. College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
2. School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
|
|
|
Abstract Rice is the most important cereal crop in China. To accurately realize the variety identification and appearance quality evaluation of geographically iconic rice is not only related to consumers' interests but also to the reputation of retailers and manufacturers, which is a widespread concern. Firstly, to realize the integrated application of milled rice variety recognition and appearance quality detection, a multi-parameter detection system for milled rice variety and appearance quality was established. The system uses an NIR spectrometer with a diffuse reflectance accessory to collect the spectral information of rice flour, which can realize the classification of milled rice varieties based on NIR spectroscopy. Multi-parameter detection of milled rice appearance quality was realized based on the image method using the Complementary metal-oxide-semiconductor (CMOS) camera. The detection objects included cracks, length/width, chalkiness, broken grains and yellow grains. Based on the above system, this paper proposed a milled rice variety classification method based on spectral-image feature model fusion to improve the classification accuracy of milled rice varieties. In this method, the NIR spectral features and multi-image features were used as the input parameters, the milled rice variety number was used as the output parameters, and a variety classification fusion model was established based on the Partial least squares (PLS) method. In the modeling process of each fusion scheme, the variable projection importance analysis (VIP) method was used to achieve the optimal selection of the input parameters. Then the optimal fusion model was determined by comparing the classification accuracy of different fusion schemes. Finally, the multi-parameter detection experiment of milled rice appearance quality and the performance comparison experiment of different milled rice variety classification methods were carried out. Experimental results showed that the detection system established in this paper could realize the multi-parameter detection of milled rice appearance quality, including broken rice rate, length-width ratio, fissured rice rate, chalky rice rate, and yellow-colored rice rate, for which the detection accuracy range was 89.2%~97.0%. The proposed milled rice variety classification method based on the spectral-image feature model fusion could improve the classification accuracy of milled rice varieties. Compared with the NIRS method, which has a better effect than the traditional methods, the classification accuracy of Wuchang, Xiangshui, Yinshui, and Yuiguang rice varieties can be improved by 2.5%~7.5% using the new variety classification method.
|
Received: 2022-04-14
Accepted: 2022-09-19
|
|
|
[1] Sha M, Gui D D,Zhang Z Y, et al. Journal of Food Measurement and Characterization, 2019, 13: 1705.
[2] Bagchi T B, Sharma S,Chattopadhyay K. Food Chemistry, 2016, 191: 21.
[3] CHEN Hao-ran, JIANG Min-lan, ZHANG Chang-jiang, et al(陈昊然, 蒋敏兰, 张长江, 等). Journal Of China Cereals and Oils Association(中国粮油学报), 2021, 36(2): 145.
[4] ZHANG Ling, YANG Cheng, LU Hui, et al(张 玲, 杨 成, 路 辉, 等). Cereals & Oils(粮食与油脂), 2021, 34(2): 97.
[5] Chen S M, Xiong J T, Guo W T, et al. Journal of Cereal Science, 2019, 88: 87.
[6] Singh S K, Vidyarthi S K, Tiwari R. Journal of Food Engineering, 2020, 274: 109828.
[7] Zareiforoush H, Minaei S, Alizadeh M R, et al. Measurement, 2015, 66: 26.
[8] SONG Xue-jian, QIAN Li-li, ZHANG Dong-jie, et al(宋雪健, 钱丽丽, 张东杰, 等). Food Science (食品科学), 2017, 38(18): 286.
[9] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰, 等). Food Science(食品科学), 2017, 38(16): 222.
[10] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰, 等). Food Science(食品科学), 2018, 39(8): 231.
[11] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al (钱丽丽, 宋雪健, 张东杰, 等). Food Science(食品科学), 2018, 39(16): 321.
[12] LIU Ya-chao, LI Yong-yu, PENG Yan-kun, et al(刘亚超, 李永玉, 彭彦昆, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(5): 1559.
|
[1] |
ZHU Wen-jing1, 2,FENG Zhan-kang1, 2,DAI Shi-yuan1, 2,ZHANG Ping-ping3,JI Wen4,WANG Ai-chen1, 2,WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
[2] |
YE Wen-chao1, LUO Shui-yang1, LI Jin-hao1, LI Zhao-rong1, FAN Zhi-wen1, XU Hai-tao1, ZHAO Jing1, LAN Yu-bin1, 2, DENG Hai-dong1*, LONG Yong-bing1, 2, 3*. Research on Classification Method of Hybrid Rice Seeds Based on the Fusion of Near-Infrared Spectra and Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2935-2941. |
[3] |
ZHANG Zhi-fen1, LIU Zi-min1, QIN Rui1, LI Geng1, WEN Guang-rui1, HE Wei-feng2. Real-Time Detection of Protective Coating Damage During Laser Shock Peening Based on ReliefF Feature Weight Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2437-2445. |
[4] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
[5] |
ZHONG Jing-jing1, 2, LIU Xiao1, 3, WANG Xue-ji1, 3, LIU Jia-cheng1, 3, LIU Hong1, 3, QI Chen1, 3, LIU Yu-yang1, 2, 3, YU Tao1, 3*. A Multidimensional Information Fusion Algorithm for Polarization
Spectrum Reconstruction Based on Nonsubsampled Contourlet
Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1254-1261. |
[6] |
BAI Xue-bing, MA Dian-kun, ZHANG Meng-jie, MA Rui-qin*. Hyperspectral Non-Destructive Analysis of Red Meat Quality: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 1993-1998. |
[7] |
WANG Cheng-kun2, 3, ZHAO Peng1, 2*, LI Xiang-hua2. Similar Wood Species Classification Within Pterocarpus Genus Using Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2247-2254. |
[8] |
PENG Ren-miao1, 2, XU Peng-peng2, ZHAO Yi-mo2, BAO Li-jun1, LI Cheng2*. Identification of Two-Dimensional Material Nanosheets Based on Deep Neural Network and Hyperspectral Microscopy Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1965-1973. |
[9] |
WANG Cheng-kun1, ZHAO Peng1,2*. Study on Simultaneous Classification of Hardwood and Softwood Species Based on Spectral and Image Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1713-1721. |
[10] |
ZHAO Peng1,2*, HAN Jin-cheng1, WANG Cheng-kun1. Wood Species Classification With Microscopic Hyper-Spectral Imaging Based on I-BGLAM Texture and Spectral Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 599-605. |
[11] |
ZHAO Peng*, TANG Yan-hui, LI Zhen-yu. Wood Species Recognition with Microscopic Hyper-Spectral Imaging and Composite Kernel SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3776-3782. |
[12] |
LU Bing1, 2, SUN Jun1*, YANG Ning1, WU Xiao-hong1, ZHOU Xin1. Prediction of Tea Diseases Based on Fluorescence Transmission Spectrum and Texture of Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2515-2521. |
[13] |
LI Jing1, GUAN Ye-peng1, 2*, LI Wei-dong3, LUO Hong-jie4. Ancient Ceramic Kiln Non-Destructive Identification Based on Multi-Wavelength Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 166-171. |
[14] |
FAN Yang-yang, QIU Zheng-jun*, CHEN Jian, WU Xiang, HE Yong . Identification of Varieties of Dried Red Jujubes with Near-Infrared Hyperspectral Imaging [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(03): 836-840. |
[15] |
KONG Xiang-bing1, SHU Ning1, TAO Jian-bin2, GONG Yan1 . A New Spectral Similarity Measure Based on Multiple Features Integration [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(08): 2166-2170. |
|
|
|
|