Detection of Mild Moldy-Core Disease in Apples by Fusing Acoustic-Vibration Signals and Visible-Near-Infrared Transmission Spectroscopy
In response to the issue of low detection accuracy for mild moldy-core disease in apples using single methods, this study proposed an approach based on the fusion of near-infrared transmission spectroscopy and acoustic-vibration technology to enhance the discriminability of mild moldy-core disease i...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
China Food Publishing Company
2024-12-01
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| Series: | Shipin Kexue |
| Subjects: | |
| Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2024-45-23-029.pdf |
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| Summary: | In response to the issue of low detection accuracy for mild moldy-core disease in apples using single methods, this study proposed an approach based on the fusion of near-infrared transmission spectroscopy and acoustic-vibration technology to enhance the discriminability of mild moldy-core disease in apples. For the near-infrared spectral signals, the impacts of different preprocessing and feature extraction methods on modeling outcomes were analyzed to select the spectral feature bands. For the acoustic-vibration signals, 7 time-domain features were optimized by using the YSV engineering test and signal analysis software as well as calculating Pearson correlation coefficients. The spectral feature bands and time-domain features were then concatenated to form a fused feature vector. Convolutional neural networks (CNN), long short-term memory (LSTM), and CNN-LSTM were employed to construct discrimination models based on single and fused features, separately. The performance analysis of the models revealed that the CNN-LSTM combination model, which integrated 15 near-infrared transmission spectral bands and 7 time-domain features, exhibited the best performance in discriminating mild moldy-core disease, with accuracy, recall, specificity, and F1 scores of 98.31%, 97.06%, 97.06%, and 97.90% on the test set, respectively. These findings demonstrate that the proposed method can effectively improve the discrimination accuracy of mild moldy-core disease in apples. |
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| ISSN: | 1002-6630 |