Authenticity Discrimination of Fragrant Rice from Wuchang Core Production Areas Based on Polarized Light Images

XU Ya; WU Yue; YU Lu

doi:10.13386/j.issn1002-0306.2023090161

Volume 45 Issue 17

Aug. 2024

Turn off MathJax

Article Contents

Abstract

References

Supplements

Science and Technology of Food Industry > 2024 > 45(17): 294-301. > DOI: 10.13386/j.issn1002-0306.2023090161

XU Ya, WU Yue, YU Lu. Authenticity Discrimination of Fragrant Rice from Wuchang Core Production Areas Based on Polarized Light Images[J]. Science and Technology of Food Industry, 2024, 45(17): 294−301. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023090161.

Citation:

PDF (3801 KB)

Authenticity Discrimination of Fragrant Rice from Wuchang Core Production Areas Based on Polarized Light Images

XU Ya^1,,
WU Yue^1, ,,
YU Lu²

1.
National Engineering Laboratory for Deep Processing of Rice and Byproduct, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha 410000, China
2.
PerkinElmer Instruments (Shanghai) Co., Ltd., Beijing 100022, China

More Information

Received Date: September 12, 2023
Available Online: June 30, 2024

Graphical Abstract

Abstract

Abstract

"Wuchang Rice" (GB/T 19266-2008) is protected by geographical indications. However, counterfeiting is common due to the significant variation in rice quality between the core producing areas and other regions. Therefore, further distinguishing core production areas within Wuchang Rice’s geographical indication product is crucial for its development, protection, and enhancement. This research focuses on Minle Township, one of the core production areas of fragrant rice in Wuchang region. This research selected fragrant rice (variety: Dahuaxiang No.2, also known as Wuyou Rice No.4) from Minle Township and compared it with the same variety from other non-Minle Township areas within Wuchang region. Polarized light images of rice grains were collected, comprising a dataset of 30000 rice grain images. Subsequently, the study employed three deep learning methods, namely DenseNet, GoogleNet, and ResNet50, which were suitable for image classification with minimal phenotypic differences, to discriminate between rice grains from Minle Township and those from non-Minle Township areas within Wuchang region. Utilizing the confusion matrix values of the models, accuracy, precision, recall, and F₁ value were calculated. The results revealed that DenseNet model performed optimally, achieving a test accuracy of 0.991. GoogleNet model followed with a test accuracy of 0.966, while ResNet50 model exhibited good performance on the training set but poor performance on the test set, showing signs of overfitting, with a test accuracy of 0.948. Although visually similar, polarized images of rice grains of the same variety from Minle Township and non-Minle Township areas within Wuchang region were effectively distinguished through the collection of polarized information images and the utilization of complex deep learning models. Therefore, the combination of polarized imaging technology and deep learning methods is a feasible approach for authenticating fragrant rice from the core production areas in Wuchang region.

FullText(HTML)

References (45)

References

[1]	侯学然, 王荣升. 五常市特色水稻品种的历史与现状研究——从松93-8到稻花香2号[J]. 中国种业,2021,312(3):16−18. [HOU Xueran, WANG Rongsheng. Study on the history and current situation of distinctive rice varieties in Wuchang city[J]. Chinese Seed Industry,2021,312(3):16−18.] doi: 10.3969/j.issn.1671-895X.2021.03.005 HOU Xueran, WANG Rongsheng. Study on the history and current situation of distinctive rice varieties in Wuchang city[J]. Chinese Seed Industry, 2021, 312（3）: 16−18. doi: 10.3969/j.issn.1671-895X.2021.03.005
[2]	李艳君, 苏中军. 优质水稻品种五优稻4号的特征特性及栽培技术[J]. 黑龙江农业科学,2009,183(3):161. [LI Yanjun, SU Zhongjun. Characteristics and cultivation techniques of high quality rice variety Wuyoudao 4[J]. Heilongjiang Agricultural Sciences,2009,183(3):161.] doi: 10.3969/j.issn.1002-2767.2009.03.066 LI Yanjun, SU Zhongjun. Characteristics and cultivation techniques of high quality rice variety Wuyoudao 4[J]. Heilongjiang Agricultural Sciences, 2009, 183（3）: 161. doi: 10.3969/j.issn.1002-2767.2009.03.066
[3]	全国知识管理标准化技术委员会地理标志分会. GB/T 19266-2008 地理标志产品五常大米[S]. 黑龙江:中国标准出版社,2009. [Geographical Indications Branch, National Technical Committee for Standardization of Knowledge Management. GB/T 19266-2008 Geographical indication product Wuchang rice[S]. Heilongjiang:Standards Press of China,2009.] Geographical Indications Branch, National Technical Committee for Standardization of Knowledge Management. GB/T 19266-2008 Geographical indication product Wuchang rice[S]. Heilongjiang: Standards Press of China, 2009.
[4]	HE X, FENG X, SUN D, et al. Rapid and nondestructive measurement of rice seed vitality of different years using near-infrared hyperspectral imaging[J]. Molecules,2019,24:2227. doi: 10.3390/molecules24122227
[5]	田青兰, 李培程, 刘利, 等. 四川不同生态区高产栽培条件下的杂交籼稻的稻米品质[J]. 作物学报,2015,41(8):1257−1268. [TIAN Qinglan, LI Peicheng, LIU Li, et al. Quality of hybrid rice under the high-yield cultivation conditions in different ecological regions of Sichuan Province, China[J]. Acta Crop Sinica,2015,41(8):1257−1268.] TIAN Qinglan, LI Peicheng, LIU Li, et al. Quality of hybrid rice under the high-yield cultivation conditions in different ecological regions of Sichuan Province, China[J]. Acta Crop Sinica, 2015, 41（8）: 1257−1268.
[6]	李友华, 王虹. 黑龙江地标品牌稻米产业发展面临的问题及对策[J]. 现代审计与会计,2019,369(12):11−19. [LI Youhua, WANG Hong. Problems and countermeasures of Heilongjiang landmark brand rice industry development[J]. Modern Auditing and Accounting,2019,369(12):11−19.] LI Youhua, WANG Hong. Problems and countermeasures of Heilongjiang landmark brand rice industry development[J]. Modern Auditing and Accounting, 2019, 369（12）: 11−19.
[7]	许慧. 湖南省粮食核心产区建设问题研究[D]. 长沙:湖南农业大学,2013. [XU Hui. The research about construction problem on core grain-producing areas of Hunan Province[D]. Changsha:Hunan Agricultural University,2013.] XU Hui. The research about construction problem on core grain-producing areas of Hunan Province[D]. Changsha: Hunan Agricultural University, 2013.
[8]	丁声俊. 对“粮食核心产区”建设的思考与建议——从“中原第一大粮仓”说起[J]. 中国粮食经济,2009(12):26−29. [DING Shengjun. Thoughts and suggestions on the construction of “Core grain producing area”—starting with “The largest grain granary in Central Plain”[J]. China’s Grain Economy,2009(12):26−29.] doi: 10.3969/j.issn.1007-4821.2009.12.017 DING Shengjun. Thoughts and suggestions on the construction of “Core grain producing area”—starting with “The largest grain granary in Central Plain”[J]. China’s Grain Economy, 2009（12）: 26−29. doi: 10.3969/j.issn.1007-4821.2009.12.017
[9]	刘文艳, 闫忠心, 郝力壮, 等. 光谱技术在食品产地溯源中的应用研究进展[J]. 食品工业科技,2023,44(21):421−430. [LIU Wenyan, YAN Zhongxin, HAO Lizhuang, et al. Research progress on application of spectral technology in food origin traceability[J]. Science and Technology of Food Industry,2023,44(21):421−430.] LIU Wenyan, YAN Zhongxin, HAO Lizhuang, et al. Research progress on application of spectral technology in food origin traceability[J]. Science and Technology of Food Industry, 2023, 44（21）: 421−430.
[10]	HE Y, BAI X, XIAO Q, et al. Detection of adulteration in food based on nondestructive analysis techniques:A review[J]. Critical Reviews in Food Science and Nutrition,2021,61(14):2351−2371. doi: 10.1080/10408398.2020.1777526
[11]	赵哲, 王嘉瑜, 伍子豪, 等. 稳定同位素在农产品溯源中的研究进展[J]. 现代农业科技,2019,744(10):213−217. [ZHAO Zhe, WANG Jiayu, WU Zihao, et al. Progress on stable isotope in traceability of agricultural products[J]. Modern Agricultural Technology,2019,744(10):213−217.] doi: 10.3969/j.issn.1007-5739.2019.10.132 ZHAO Zhe, WANG Jiayu, WU Zihao, et al. Progress on stable isotope in traceability of agricultural products[J]. Modern Agricultural Technology, 2019, 744（10）: 213−217. doi: 10.3969/j.issn.1007-5739.2019.10.132
[12]	CAJKA T, HAJSLOVA J. Volatile compounds in food authenticity and traceability testing[J]. Chemical & Functional Properties of Food Components,2011,355–412.
[13]	白扬, 谭丽芹, 赵姗姗, 等. 大米产地溯源和真实性研究进展[J]. 安徽农业科学,2019,49(18):22−29. [BAI Yang, TAN Liqin, ZHAO Shanshan, et al. Research progress of rice origin traceability and authenticity[J]. Anhui Agricultural Sciences,2019,49(18):22−29.] BAI Yang, TAN Liqin, ZHAO Shanshan, et al. Research progress of rice origin traceability and authenticity[J]. Anhui Agricultural Sciences, 2019, 49（18）: 22−29.
[14]	胡圣英, 任红波, 张军, 等. 大米产地溯源方法研究进展[J]. 中国农学通报,2020,36(14):148−155. [HU Shengying, REN Hongbo, ZHANG Jun, et al. Traceability method of rice origin:Research progress[J]. Bulletin of Chinese Agronomy,2020,36(14):148−155.] HU Shengying, REN Hongbo, ZHANG Jun, et al. Traceability method of rice origin: Research progress[J]. Bulletin of Chinese Agronomy, 2020, 36（14）: 148−155.
[15]	WADOOD S A, NIE J, LI C, et al. Rice authentication:An overview of different analytical techniques combined with multivariate analysis[J]. Journal of Food Composition and Analysis,2022,112:104677. doi: 10.1016/j.jfca.2022.104677
[16]	CHEN J L, M ZHANG, XU B G, et al. Artificial intelligence assisted technologies for controlling the drying of fruits and vegetables using physical fields:A review[J]. Trends in Food Science & Technology,2020,105:251−60.
[17]	GAO J M, LIU C Z, HAN J Y, et al. Identification method of wheat cultivars by using a convolutional neural network combined with images of multiple growth periods of wheat[J]. Symmetry,2021,13(11):2012. doi: 10.3390/sym13112012
[18]	杨红云, 黄琼, 孙爱珍, 等. 基于卷积神经网络和支持向量机的水稻种子图像分类识别[J]. 中国粮油学报,2021,36(12):144−150. [YANG Hongyun, HUANG Qiong, SUN Aizhen, et al. Rice seed image classification and recognition based on CNN_SVM[J]. Chinese Journal of Grain and Oil,2021,36(12):144−150.] doi: 10.3969/j.issn.1003-0174.2021.12.022 YANG Hongyun, HUANG Qiong, SUN Aizhen, et al. Rice seed image classification and recognition based on CNN_SVM[J]. Chinese Journal of Grain and Oil, 2021, 36（12）: 144−150. doi: 10.3969/j.issn.1003-0174.2021.12.022
[19]	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. doi: 10.3390/app8020212
[20]	WANG B, LU A, YU L. A multi-kernel channel attention combined with convolutional neural network to identify spectral information for tracing the origins of rice samples[J]. Analytical Methods:Advancing Methods and Applications,2023,15(2):179−186.
[21]	KOKLU M, CINAR I, TASPINAR Y S. Classification of rice varieties with deep learning methods[J]. Computers and Electronics in Agriculture,2021,187(3):106285.
[22]	WENG S Z, TANG P P, YUAN H C, et al. Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion[J]. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy,2020,234:118237. doi: 10.1016/j.saa.2020.118237
[23]	孙瑞, 孙晓兵, 刘晓, 等. 基于注意力机制的偏振成像目标分类方法[J]. 光学学报,2021,41(16):103−111. [SUN Rui, SUN Xiaobing, LIU Xiao, et al. Polarimetric imaging target classification method based on attention mechanism[J]. Acta Optica Sinica,2021,41(16):103−111.] SUN Rui, SUN Xiaobing, LIU Xiao, et al. Polarimetric imaging target classification method based on attention mechanism[J]. Acta Optica Sinica, 2021, 41（16）: 103−111.
[24]	周强国, 黄志明, 周炜. 偏振成像技术的研究进展及应用[J]. 红外技术,2021,43(9):817−828. [ZHOU Qiangguo, HUANG Zhiming, ZHOU Wei. Research progress and application of polarization imaging technology[J]. Infrared Technology,2021,43(9):817−828.] ZHOU Qiangguo, HUANG Zhiming, ZHOU Wei. Research progress and application of polarization imaging technology[J]. Infrared Technology, 2021, 43（9）: 817−828.
[25]	ZHANG L, YUAN H W, LI X M. Active polarization imaging method for latent fingerprint detection[J]. Opt Quant Electron,2018,50:353. doi: 10.1007/s11082-018-1616-8
[26]	汪靓, 杨宇, 黄敏, 等. 基于偏振成像技术的油桃机械损伤检测[J]. 激光技术,2022,46(6):841−849. [WANG Liang, YANG Yu, HUANG Min, et al. Mechanical damage detection of nectarine based on polarization imaging[J]. Laser Technology,2022,46(6):841−849.] doi: 10.7510/jgjs.issn.1001-3806.2022.06.021 WANG Liang, YANG Yu, HUANG Min, et al. Mechanical damage detection of nectarine based on polarization imaging[J]. Laser Technology, 2022, 46（6）: 841−849. doi: 10.7510/jgjs.issn.1001-3806.2022.06.021
[27]	黄世钊, 陶彦辉. 基于偏振信息的豆类特征提取方法[J]. 佳木斯大学学报(自然科学版),2020,38(2):130−132. [HUANG Shizhao, TAO Yanhui. Bean feature extraction method based on polarization information[J]. Journal of Jiamusi University (Natural Science Edition),2020,38(2):130−132.] HUANG Shizhao, TAO Yanhui. Bean feature extraction method based on polarization information[J]. Journal of Jiamusi University （Natural Science Edition）, 2020, 38（2）: 130−132.
[28]	FERNÁNDEZ-CANTO N, ROMERO-RODRÍGUEZ M Á, RAMOS-CABRER A M, et al. Polarized light microscopy guarantees the use of autochthonous wheat in the production of flour for the protected geographical indication ‘Galician Bread’[J]. Food Control,2023,147:109597. doi: 10.1016/j.foodcont.2022.109597
[29]	TOMITA H, FUKUOKA M, TAKEMORI T, et al. Development of the visualization and quantification method of the rice soaking process by using the digital microscope[J]. Journal of Food Engineering,2019,243:33−38. doi: 10.1016/j.jfoodeng.2018.08.034
[30]	程宇琼, 卢伟, 罗慧, 等. 基于连续偏振光谱技术与嵌入型灰色神经网络的稻种发芽率检测方法研究[J]. 光学学报,2015,35(12):296−304. [CHENG Yuqiong, LU Wei, LUO Hui, et al. Study on prediction of rice seed germination rate by using continuous polarization spectroscopy and inlaid grey neural network[J]. Acta Optica Sinica,2015,35(12):296−304.] CHENG Yuqiong, LU Wei, LUO Hui, et al. Study on prediction of rice seed germination rate by using continuous polarization spectroscopy and inlaid grey neural network[J]. Acta Optica Sinica, 2015, 35（12）: 296−304.
[31]	陈文博, 刘昌华, 刘春苔, 等. 基于GoogLeNet的稻米品种识别与碎米检测[J]. 中国粮油学报,2023,38(2):146−152. [CHEN Wenbo, LIU Changhua, LIU Chuntai, et al. Identification rice varieties and broken rice based on GoogLeNet[J]. Journal of Cereals and Oils,2023,38(2):146−152.] doi: 10.3969/j.issn.1003-0174.2023.02.022 CHEN Wenbo, LIU Changhua, LIU Chuntai, et al. Identification rice varieties and broken rice based on GoogLeNet[J]. Journal of Cereals and Oils, 2023, 38（2）: 146−152. doi: 10.3969/j.issn.1003-0174.2023.02.022
[32]	樊湘鹏, 周建平, 许燕, 等. 数据集对基于深度学习的作物病害识别有效性影响[J]. 中国农机化学报,2021,42(1):192−200. [FAN Xiangpeng, ZHOU Jianping, XU Yan, et al. Influence of data set on effectiveness of crop disease recognition based on deep learning[J]. Chinese Journal of Agricultural Mechanization,2021,42(1):192−200.] FAN Xiangpeng, ZHOU Jianping, XU Yan, et al. Influence of data set on effectiveness of crop disease recognition based on deep learning[J]. Chinese Journal of Agricultural Mechanization, 2021, 42（1）: 192−200.
[33]	IANDOLA F, MOSKEWICZ M, KARAYEV, et al. Densenet:Implementing efficient convnet descriptor pyramids[J]. arXiv preprint arXiv,2014,1404:1869.
[34]	YU C, ZHOU L, WANG X, et al. Hyperspectral detection of unsound kernels of wheat based on convolutional neural network[J]. Food Science,2017,38(24):283−287.
[35]	史婷婷, 张小波, 郭兰萍, 等. 基于深度卷积神经网络的仿野生种植金银花遥感识别方法研究[J]. 中国中药杂志,2020,45(23):5658−5662. [SHI Tingting, ZHANG Xiaobo, GUO Lanping, et al. Research on remote sensing recognition of wild planted Lonicera japonica based on deep convolutional neural network[J]. Chinese Journal of Traditional Chinese Medicine,2020,45(23):5658−5662.] SHI Tingting, ZHANG Xiaobo, GUO Lanping, et al. Research on remote sensing recognition of wild planted Lonicera japonica based on deep convolutional neural network[J]. Chinese Journal of Traditional Chinese Medicine, 2020, 45（23）: 5658−5662.
[36]	梁万杰, 冯辉, 江东, 等. 高光谱图像结合深度学习的油菜菌核病早期识别[J]. 光谱学与光谱分析,2023,43(7):2220−2225. [LANG Wanjie, FENG Hui, JING Dong, et al. Early recognition of sclerotinia stem rot on oilseed rape by hyperspectral imaging combined with deep learning[J]. Spectroscopy and Spectral Analysis,2023,43(7):2220−2225.] LANG Wanjie, FENG Hui, JING Dong, et al. Early recognition of sclerotinia stem rot on oilseed rape by hyperspectral imaging combined with deep learning[J]. Spectroscopy and Spectral Analysis, 2023, 43（7）: 2220−2225.
[37]	资彩飞, 曹志勇, 许佳俊, 等. 基于深度学习的水稻稻瘟病识别研究[J]. 现代农业科技,2022(1):111−113,118. [ZI Caifei, CAO Zhiyong, XU Jiajun, et al. Research on rice blast identification based on deep learning[J]. Modern Agricultural Science and Technology,2022(1):111−113,118.] doi: 10.3969/j.issn.1007-5739.2022.01.034 ZI Caifei, CAO Zhiyong, XU Jiajun, et al. Research on rice blast identification based on deep learning[J]. Modern Agricultural Science and Technology, 2022（1）: 111−113,118. doi: 10.3969/j.issn.1007-5739.2022.01.034
[38]	陈伟文, 邝祝芳, 王忠伟. 基于卷积神经网络的种苗病害识别方法[J]. 中南林业科技大学学报,2022,42(7):35−43. [CHEN Weiwen, KUANG Zhufang, WANG Zhongwei. Method of seed disease recognition based on convolutional neural network[J]. Journal of Central South University of Forestry and Technology,2022,42(7):35−43.] CHEN Weiwen, KUANG Zhufang, WANG Zhongwei. Method of seed disease recognition based on convolutional neural network[J]. Journal of Central South University of Forestry and Technology, 2022, 42（7）: 35−43.
[39]	陶砾, 杨朔, 杨威. 深度学习的模型搭建及过拟合问题的研究[J]. 计算机时代,2018(2):14−17,21. [TAO Li, YANG Shuo, YANG Wei. Research on model building and over-fitting of deep learning[J]. Computer Age,2018(2):14−17,21.] TAO Li, YANG Shuo, YANG Wei. Research on model building and over-fitting of deep learning[J]. Computer Age, 2018（2）: 14−17,21.
[40]	杨雨昂, 闫星辰, 肖潇, 等. 基于DenseNet的西瓜叶片病虫害识别模型[J]. 电脑与信息技术,2019,31(2):19−23. [YANG Yuang, YAN Xingchen, XIAO Xiao, et al. A recognition model of watermelon leaf diseases based on DenseNet[J]. Computer and Information Technology,2019,31(2):19−23.] YANG Yuang, YAN Xingchen, XIAO Xiao, et al. A recognition model of watermelon leaf diseases based on DenseNet[J]. Computer and Information Technology, 2019, 31（2）: 19−23.
[41]	李恩霖, 谢秋菊, 苏中滨, 等. 基于深度学习的玉米叶片病斑识别方法研究[J]. 智慧农业导刊,2021,1(10):1−10. [LI Enlin, XIE Qiuju, SU Zhongbin, et al. Research on corn disease identification method based on deep learning[J]. Journal of Intelligent Agriculture,2021,1(10):1−10.] LI Enlin, XIE Qiuju, SU Zhongbin, et al. Research on corn disease identification method based on deep learning[J]. Journal of Intelligent Agriculture, 2021, 1（10）: 1−10.
[42]	尹显明, 棘玉, 张日清, 等. 深度学习在基于叶片的油茶品种识别中的研究[J]. 南京林业大学学报(自然科学版),2023,47(3):29−36. [YIN Xianming, JIAN Yu, ZHANG Riqing, et al. Research on recognition of Camellia oleifera leaf varieties based on deep learning[J]. Journal of Nanjing Forestry University (Natural Science Edition),2023,47(3):29−36.] doi: 10.12302/j.issn.1000-2006.202112037 YIN Xianming, JIAN Yu, ZHANG Riqing, et al. Research on recognition of Camellia oleifera leaf varieties based on deep learning[J]. Journal of Nanjing Forestry University （Natural Science Edition）, 2023, 47（3）: 29−36. doi: 10.12302/j.issn.1000-2006.202112037
[43]	林珑, 吴静珠, 刘翠玲, 等. 基于模型集群的东北/非东北大米产地高光谱鉴别方法研究[J]. 光谱学与光谱分析,2020,40(3):905−910. [LIN Long, WU Jingzhu, LIU Cuiling, et al. Study on hyperspectral identification method of rice origin in northeast/non-northeast China based on conjunctive model[J]. Spectroscopy and Spectral Analysis,2020,40(3):905−910.] LIN Long, WU Jingzhu, LIU Cuiling, et al. Study on hyperspectral identification method of rice origin in northeast/non-northeast China based on conjunctive model[J]. Spectroscopy and Spectral Analysis, 2020, 40（3）: 905−910.
[44]	TONG P J, KEVIN L J, WEI T T, et al. Rapid identification of the variety and geographical origin of Wuyou No.4 rice by fourier transform near-infrared spectroscopy coupled with chemometrics[J]. Journal of Cereal Science,2021,102:103322. doi: 10.1016/j.jcs.2021.103322
[45]	ZHANG C, JIA L, JIN B, et al. Identification of rice seed varieties based on near-infrared hyperspectral imaging technology combined with deep learning[J]. ACS Omega,2022,7(6):4735−4749. doi: 10.1021/acsomega.1c04102