• EI
  • Scopus
  • 食品科学与工程领域高质量科技期刊分级目录第一方阵T1
  • DOAJ
  • EBSCO
  • 北大核心期刊
  • 中国核心学术期刊RCCSE
  • JST China
  • FSTA
  • 中国精品科技期刊
  • 中国农业核心期刊
  • CA
  • WJCI
  • 中国科技核心期刊CSTPCD
  • 中国生物医学SinoMed
中国精品科技期刊2020
徐娅,吴跃,郁露. 基于偏振光图像的五常核心产区稻花香米真实性判别[J]. 食品工业科技,2024,45(17):1−8. doi: 10.13386/j.issn1002-0306.2023090161.
引用本文: 徐娅,吴跃,郁露. 基于偏振光图像的五常核心产区稻花香米真实性判别[J]. 食品工业科技,2024,45(17):1−8. 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): 1−8. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023090161.
Citation: 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): 1−8. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023090161.

基于偏振光图像的五常核心产区稻花香米真实性判别

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

  • 摘要: “五常大米”(GB/T 19266-2008)享有地理标志保护,但由于其核心产区内外稻米品质差异较大及频繁的仿冒现象,进一步区分五常大米核心产区将有助于品牌发展、保护和提升。本论文以五常稻花香大米核心产区之一的民乐乡为例,选取民乐乡与五常地区非民乐乡不同产地的相同稻花香2号(五优稻4号)品种稻米,采集其偏振光图像,数据集总共包含3万张稻米颗粒图片;选择适合识别图像表型差异小的三种深度学习方法DenseNet、GoogleNet、ResNet50,进行民乐乡与非民乐乡的稻米籽粒偏振图像分类判别;随后,利用模型的混淆矩阵值,计算出每个模型的准确率、精确率、召回率和F1值。实验结果表明,DenseNet模型性能最优,平均准确率达到0.991;GoogleNet模型次之,平均准确率为0.966;ResNet50模型在训练集上表现良好,但在测试集上表现较差,且出现过拟合现象,平均准确率为0.946。本研究中五常的民乐乡与非民乐乡相同品种糙米图像肉眼观察相似度较高,但通过采集其偏振信息图片并且利用复杂的深度学习模型能够实现较好区分。所以,利用偏振成像技术结合深度学习的方法对五常核心产区稻花香米进行真实性判别具有可行性。

     

    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 score were calculated. The results revealed that DenseNet model performed optimally, achieving a test accuracy of 98.5%. GoogleNet model followed with a test accuracy of 96%, 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 94%. 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.

     

/

返回文章
返回