ArticleFood chemistry: X2025
Multimodal deep learning with hyperspectral imaging for accurate origin classification of wolfberries.
Article in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Use of Machine Learning Techniques for Fertilization Traceability Discrimination via Core Quality Indicators of Korla Fragrant Pear Fruits.Foods (Basel, Switzerland) · 2026Article
- Hyperspectral-Imaging-Based ECNN-1D for Accurate Origin Classification of Fragrant Pears.Foods (Basel, Switzerland) · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate classification of wolfberry geographical origin is essential for assessing its nutritional and medicinal properties. A multimodal convolutional neural network (MTCNN) with a cross-attention mechanism was proposed to effectively fuse spectral and image features, achieving a test accuracy of 99.88 %. Traditional feature extraction methods such as and principal component analysis (PCA) were combined with support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. The SVM model using gradient boosting decision tree (GBDT) -extracted spectral features and fused image data achieved the highest accuracy of 96.68 %. The comparison results highlight the superior performance of multimodal deep learning over conventional methods, demonstrating its potential for robust agricultural product traceability, authentication, and quality assurance. Beyond achieving higher accuracy, our model improves upon previous architectures by reducing computational complexity through the use of a simplified attention mechanism and enhancing interpretability, making the model more efficient and accessible for practical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.