A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Wijewardane, Nuwan K; Zhang, Huichun; Yang, Jinliang; Schnable, James C; Schachtman, Daniel P; Ge, Yufeng

Wijewardane, Nuwan K; Zhang, Huichun; Yang, Jinliang; Schnable, James C; Schachtman, Daniel P; Ge, Yufeng.

Afiliación

Wijewardane NK; Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, USA.
Zhang H; College of Mechanical and Electrical Engineering, Nanjing Forestry University, Nanjing, China.
Yang J; Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, China.
Schnable JC; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
Schachtman DP; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA.
Ge Y; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.

J Exp Bot ; 74(14): 4050-4062, 2023 08 03.

Article en En | MEDLINE | ID: mdl-37018460

Asunto(s)

Clorofila; Grano Comestible; Clorofila/metabolismo; Fenotipo; Grano Comestible/metabolismo; Hojas de la Planta/metabolismo; Análisis de los Mínimos Cuadrados; Glycine max/metabolismo

Palabras clave

Biochemical traits; camelina; extra-weighted spiking; high-throughput phenotyping; leaf hyperspectral reflectance; machine-learning; maize; partial least squares regression; sorghum; soybean; trait modeling

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Grano Comestible / Clorofila Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Exp Bot Asunto de la revista: BOTANICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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