Cargando…
A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning
BACKGROUND: Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput,...
Autores principales: | Tu, Keling, Wen, Shaozhe, Cheng, Ying, Xu, Yanan, Pan, Tong, Hou, Haonan, Gu, Riliang, Wang, Jianhua, Wang, Fengge, Sun, Qun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188178/ https://www.ncbi.nlm.nih.gov/pubmed/35690826 http://dx.doi.org/10.1186/s13007-022-00918-7 |
Ejemplares similares
-
Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds
por: Xu, Yanan, et al.
Publicado: (2022) -
Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
por: Yang, Huan, et al.
Publicado: (2023) -
Application of hyperspectral imaging and chemometrics for variety classification of maize seeds
por: Zhao, Yiying, et al.
Publicado: (2018) -
Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds
por: Zhang, Xiaolei, et al.
Publicado: (2012) -
Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds
por: Bai, Xiulin, et al.
Publicado: (2020)