Efficient, automatic, and optimized portable Raman-spectrum-based pesticide detection system

Kuo, Ping-Huan; Chang, Chen-Wen; Tseng, Yung-Ruen; Yau, Her-Terng

doi:10.1016/j.saa.2023.123787

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

2024

DOI: 10.1016/j.saa.2023.123787

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Efficient, automatic, and optimized portable Raman-spectrum-based pesticide detection system

Ping-Huan Kuo,

Chen-Wen Chang,

Yung-Ruen Tseng

et al.

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Cited by 3 publications

References 33 publications

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Enhanced piezoelectric performance of PVDF nanofibers by biomimicking the Spider’s long liquid transport

Qian,

He,

2024

Chemical Engineering Journal

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Enhanced piezoelectric performance of PVDF nanofibers by biomimicking the Spider’s long liquid transport

Qian,

He,

2024

Chemical Engineering Journal

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Synthetic minority oversampling and iterative fluorescence-suppression integrated algorithm for Raman spectrum pesticide detection system

Kuo,

Chang,

Chang

et al. 2025

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Surface roughness measurement using microscopic vision and deep learning

Shang,

Lieping,

Gepreel

et al. 2024

Front. Phys.

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Due to the self-affine property of the grinding surface, the sample images with different roughness captured by the micron-scale camera exhibit certain similarities. This similarity affects the prediction accuracy of the deep learning model. In this paper, we propose an illumination method that can mitigate the impact of self-affinity using the two-scale fractal theory as a foundation. This is followed by the establishment of a machine vision detection method that integrates a neural network and correlation function. Initially, a neural network is employed to categorize and forecast the microscopic image of the workpiece surface, thereby determining its roughness category. Subsequently, the corresponding correlation function is determined in accordance with the established roughness category. Finally, the surface roughness of the workpiece was calculated based on the correlation function. The experimental results demonstrate that images obtained using this lighting method exhibit significantly enhanced accuracy in neural network classification. In comparison to traditional lighting methods, the accuracy of this method on the micrometer scale has been found to have significantly increased from approximately 50% to over 95%. Concurrently, the mean squared error (MSE) of the surface roughness calculated by the proposed method does not exceed 0.003, and the mean relative error (MRE) does not exceed 5%. The two-scale fractal geometry offers a novel approach to image processing and machine learning, with significant potential for advancement.

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Efficient, automatic, and optimized portable Raman-spectrum-based pesticide detection system

Cited by 3 publications

References 33 publications

Enhanced piezoelectric performance of PVDF nanofibers by biomimicking the Spider’s long liquid transport

Enhanced piezoelectric performance of PVDF nanofibers by biomimicking the Spider’s long liquid transport

Synthetic minority oversampling and iterative fluorescence-suppression integrated algorithm for Raman spectrum pesticide detection system

Surface roughness measurement using microscopic vision and deep learning

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