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Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

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Název: Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels
Autoři: Mengqing Qiu, Shouguo Zheng, Le Tang, Xujin Hu, Qingshan Xu, Ling Zheng, Shizhuang Weng
Zdroj: Foods, Vol 11, Iss 578, p 578 (2022)
Informace o vydavateli: MDPI AG
Rok vydání: 2022
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: Raman spectroscopy, Fusarium head blight (FHB), wheat kernels, inception network, residual module, channel attention module, Chemical technology, TP1-1185
Popis: Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://www.mdpi.com/2304-8158/11/4/578; https://doaj.org/toc/2304-8158; https://doaj.org/article/0f2d4e8331b94b48ac6176dd739afe48
DOI: 10.3390/foods11040578
Dostupnost: https://doi.org/10.3390/foods11040578
https://doaj.org/article/0f2d4e8331b94b48ac6176dd739afe48
Přístupové číslo: edsbas.3EC5B3AB
Databáze: BASE
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  – Url: https://doi.org/10.3390/foods11040578#
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  Group: Ti
  Data: Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels
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  Data: <searchLink fieldCode="AR" term="%22Mengqing+Qiu%22">Mengqing Qiu</searchLink><br /><searchLink fieldCode="AR" term="%22Shouguo+Zheng%22">Shouguo Zheng</searchLink><br /><searchLink fieldCode="AR" term="%22Le+Tang%22">Le Tang</searchLink><br /><searchLink fieldCode="AR" term="%22Xujin+Hu%22">Xujin Hu</searchLink><br /><searchLink fieldCode="AR" term="%22Qingshan+Xu%22">Qingshan Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Ling+Zheng%22">Ling Zheng</searchLink><br /><searchLink fieldCode="AR" term="%22Shizhuang+Weng%22">Shizhuang Weng</searchLink>
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  Data: Foods, Vol 11, Iss 578, p 578 (2022)
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  Data: MDPI AG
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  Data: 2022
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22Raman+spectroscopy%22">Raman spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Fusarium+head+blight+%28FHB%29%22">Fusarium head blight (FHB)</searchLink><br /><searchLink fieldCode="DE" term="%22wheat+kernels%22">wheat kernels</searchLink><br /><searchLink fieldCode="DE" term="%22inception+network%22">inception network</searchLink><br /><searchLink fieldCode="DE" term="%22residual+module%22">residual module</searchLink><br /><searchLink fieldCode="DE" term="%22channel+attention+module%22">channel attention module</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+technology%22">Chemical technology</searchLink><br /><searchLink fieldCode="DE" term="%22TP1-1185%22">TP1-1185</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.
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  Data: https://www.mdpi.com/2304-8158/11/4/578; https://doaj.org/toc/2304-8158; https://doaj.org/article/0f2d4e8331b94b48ac6176dd739afe48
– Name: DOI
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  Data: 10.3390/foods11040578
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      – SubjectFull: Raman spectroscopy
        Type: general
      – SubjectFull: Fusarium head blight (FHB)
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      – SubjectFull: wheat kernels
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      – SubjectFull: residual module
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      – SubjectFull: channel attention module
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