Application of near-infrared spectroscopy and CNN-TCN for the identification of foreign fibers in cotton layers

Du, Yu; Li, Xueliang; Yu-hong, DU; Zuo, Hengli

doi:10.1080/15440478.2023.2172638

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Missing values were addressed using cubic spline interpolation implemented in MATLAB R2022a software. To mitigate instrument noise effects on the spectral curve and enhance the signal-to-noise ratio, Savitzky-Golay convolution smoothing was applied in the research methodology [25]. The findings demonstrate the effective filtering of external noise, revealing characteristic peaks at 1400 nm and 1985 nm.…”

Section: • Calibration Data Acquisitionmentioning

confidence: 93%

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Zhang,

Wang,

Fan

2024

Applied Sciences

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Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection.

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Section: • Calibration Data Acquisitionmentioning

confidence: 93%

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Zhang,

Wang,

Fan

2024

Applied Sciences

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“…In the current landscape, a greater concentration of research endeavors are directed towards the detection of foreign fibers within cotton. Various technical methods, including machine vision ( Zhang et al., 2011 ; Zhang and Li, 2014 ), hyperspectral imaging ( Liu et al., 2022 ), and near-infrared spectroscopy ( Du et al., 2023 ), have been harnessed to detect foreign fibers present in both lint and seed cotton samples. Researchers have also delved into studies focusing on the identification of non-foreign fiber impurities, specifically plant-based impurities, within both seed and lint cotton.…”

Section: Introductionmentioning

confidence: 99%

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Li,

Zhou,

Zhang

et al. 2024

Front. Plant Sci.

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Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.

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Cotton-YOLO: Improved YOLOV7 for rapid detection of foreign fibers in seed cotton

Li,

Ma,

et al. 2024

Computers and Electronics in Agriculture

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Application of near-infrared spectroscopy and CNN-TCN for the identification of foreign fibers in cotton layers

Cited by 9 publications

References 26 publications

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Cotton-YOLO: Improved YOLOV7 for rapid detection of foreign fibers in seed cotton

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