Application of near-infrared spectroscopy and CNN-TCN for the identification of foreign fibers in cotton layers
| Autor: | Yu Hong Du, Xueliang Li, Weijia Ren, Hengli Zuo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Natural Fibers, Vol 20, Iss 1 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1544-0478 1544-046X 15440478 |
| DOI: | 10.1080/15440478.2023.2172638 |
| Popis: | Foreign fibers in cotton layers have a particular impact on the quality of the cotton. Traditional image processing methods are ineffective in detecting foreign fibers in cotton layers, which are time-consuming and costly. In order to identify foreign fibers effectively, a classification and identification method for foreign fibers in cotton layers was proposed based on NIR spectroscopy and CNN-TCN. In this study, near-infrared spectroscopy ranging from 780 nm to 2360 nm was used to identify the type of foreign fibers. Savitzky-Golay smoothing was used to preprocess spectroscopy data, and LightGBM-ANOVA was used to determine optimal wavelengths. Preprocessed spectral data extracted spectral features through the 1D convolutional neural network(1D-CNN). Then Temporal convolutional neural network (TCN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and 1D-CNN were used to establish classification models. Compared with other time series models, CNN-TCN methods obtained better performances with the classification accuracy of over 99% in the test set and the shorter training time. The overall results illustrated that near-infrared spectral combined with the CNN-TCN method was efficient and accurate for identifying foreign fibers in the cotton layer. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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