ArticleFoods (Basel, Switzerland)2022
Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- Weakly Supervised Fine-Grained Discrimination of Wheat Mold Using Local RGB-HSI Fusion.Foods (Basel, Switzerland) · 2026Article
- PlasticAnalytics: A Deep Learning-Powered Spectral Library and Analytical Suite.Environmental science & technology · 2026Article
- Machine learning-based prediction of preterm birth risk using methylation changes in neonatal cord blood CpG sites.BMC pregnancy and childbirth · 2025Article
- Survey and Identification of Fusarium Head Blight Pathogens of Wheat in the Western Cape Region of South Africa.Pathogens (Basel, Switzerland) · 2025Article
- Unveiling the potential: Harnessing spectral technologies for enhanced protein and gluten content prediction in wheat grains and flour.Current research in food science · 2025Article
- Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective.Plants (Basel, Switzerland) · 2024Review
- Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders.Biomedical optics express · 2024Article
- Diagnosing arsenic-mediated biochemical responses in rice cultivars using Raman spectroscopy.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
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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