Cargando…
Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography
The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits...
Autores principales: | Hu, Weijuan, Zhang, Can, Jiang, Yuqiang, Huang, Chenglong, Liu, Qian, Xiong, Lizhong, Yang, Wanneng, Chen, Fan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706343/ https://www.ncbi.nlm.nih.gov/pubmed/33313550 http://dx.doi.org/10.34133/2020/3414926 |
Ejemplares similares
-
Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline
por: Feng, Hui, et al.
Publicado: (2017) -
X-ray Micro-Computed Tomography for Nondestructive Three-Dimensional (3D) X-ray Histology
por: Katsamenis, Orestis L., et al.
Publicado: (2019) -
Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer
por: Yang, Wanneng, et al.
Publicado: (2015) -
A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits
por: Wu, Di, et al.
Publicado: (2021) -
Field rice panicle detection and counting based on deep learning
por: Wang, Xinyi, et al.
Publicado: (2022)