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

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 se...

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Bibliographic Details
Published in:Foods 2022-02, Vol.11 (4), p.578
Main Authors: Qiu, Mengqing, Zheng, Shouguo, Tang, Le, Hu, Xujin, Xu, Qingshan, Zheng, Ling, Weng, Shizhuang
Format: Article
Language:English
Subjects:
Agricultural production
Blight
channel attention module
Decision making
Decision trees
Deep learning
Discriminant analysis
Feature extraction
Feature maps
Food science
Fusarium head blight
Fusarium head blight (FHB)
inception network
Infections
Kernels
Machine learning
Plant diseases
Quality assurance
Raman spectroscopy
residual module
Spectroscopy
Support vector machines
Wheat
wheat kernels
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Summary: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.
ISSN:2304-8158
2304-8158
DOI:10.3390/foods11040578