Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model.

Liu, Weihua; Zeng, Shan; Wu, Guiju; Li, Hao; Chen, Feifei

Liu, Weihua; Zeng, Shan; Wu, Guiju; Li, Hao; Chen, Feifei.

Afiliación

Liu W; School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China.
Zeng S; School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China.
Wu G; The Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430023, China.
Li H; School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China.
Chen F; School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China.

Sensors (Basel) ; 21(13)2021 Jun 26.

Article en En | MEDLINE | ID: mdl-34206783

Asunto(s)

Oryza; Algoritmos; Modelos Logísticos; Semillas; Tecnología

Palabras clave

LASSO logistic regression model; grey-scale image; hyperspectral imaging; seed purity identification; wavelength band selection

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza Tipo de estudio: Diagnostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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