Cognitive spectroscopy for the classification of rice varieties: A comparison of machine learning and deep learning approaches in analysing long-wave near-infrared hyperspectral images of brown and milled samples

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dc.contributor.authorJiraporn Onmankhong
dc.contributor.authorTe Ma
dc.contributor.authorTetsuya Inagaki
dc.contributor.authorPanmanas Sirisomboon
dc.contributor.authorSatoru Tsuchikawa
dc.date.accessioned2026-05-08T19:14:38Z
dc.date.issued2022-2-21
dc.identifier.doi10.1016/j.infrared.2022.104100
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14642
dc.publisherInfrared Physics & Technology
dc.subjectSpectroscopy and Chemometric Analyses
dc.subjectSpectroscopy Techniques in Biomedical and Chemical Research
dc.subjectAdvanced Chemical Sensor Technologies
dc.titleCognitive spectroscopy for the classification of rice varieties: A comparison of machine learning and deep learning approaches in analysing long-wave near-infrared hyperspectral images of brown and milled samples
dc.typeArticle

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