Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology

Yin, Jianwei; Yang, Yinglin; Hong, Weibin; Cai, Yefan; Yu, Xiangyang

doi:10.3390/app9163279

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Unlike soybean and paddy, tea is irregular in shape and difficult to be completely dispersed by vibration feeder, making it difficult to detect impurities using geometrical feature. The spectral imaging techniques is commonly used in tea industry [25][26][27][28][29]. Spectral imaging techniques mainly include Multispectral imaging(MSI) and Hyperspectral imaging(HSI).…”

Section: Of 15mentioning

confidence: 99%

Design of a Mulitmodal Detection System and Its First Application in Tea Selection Process

Kuang,

Yu,

Guo

et al. 2024

Preprint

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A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral feature and color feature at the same time, and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu ’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu ’er tea were obtained by this system and used for pixel classification. The experimental results show that the accuracy of Support Vector Machine (SVM) model based on combined features is 93%, which is 7% higher than that based on only spectral features. By applying median filtering algorithm and contour detection algorithm to the label matrix extracted from pixel-classified images, 8 impurities except hair were detected successfully. Moreover, taking advantage of the high resolution of visible light camera, small impurities can be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it is proved that the system can obtain multiple images to allow a more detailed and comprehensive understanding of the detected items, and has excellent ability to detect small impurities.

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Section: Of 15mentioning

confidence: 99%

Design of a Mulitmodal Detection System and Its First Application in Tea Selection Process

Kuang,

Yu,

Guo

et al. 2024

Preprint

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show abstract

“…In addition to machine vision methods, spectral imaging techniques are commonly used in intelligent detection research [27][28][29][30][31]. Spectral imaging technology combines twodimensional imaging technology and spectral technology and can obtain the spatial and spectral information of objects at the same time.…”

Section: Introductionmentioning

confidence: 99%

Design of a Multimodal Detection System Tested on Tea Impurity Detection

Kuang,

Yu,

Guo

et al. 2024

Remote Sensing

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A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.

show abstract