Attention-enhanced residual autoencoder for NIR spectral feature extraction and classification of grain varieties

Accurate identification of grain cultivars is critical for improving crop yields, streamlining agricultural workflows, and ensuring global food security. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive solution for grain classification. However, its effectiveness hinges on extractin...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 32750 - 18
Hauptverfasser:	Kabiso, Abel Chernet, Ko, Cheng-Hao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 24.09.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	631/449 639/166 639/705 Agricultural practices Attention mechanisms Autoencoder Barley Classification Crop yield Cultivars Data analysis Datasets Deep learning Food security Grain Grain classification Humanities and Social Sciences Infrared spectroscopy Machine learning multidisciplinary Neural networks PCA Performance evaluation Portable NIR spectroscopy Science Science (multidisciplinary) Smartphones Sorghum Spectrometers Spectrum analysis Wavelet transforms Grain classification Attention mechanisms Autoencoder Portable NIR spectroscopy PCA
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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