Cargando…
Self-supervised maize kernel classification and segmentation for embryo identification
INTRODUCTION: Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing g...
Autores principales: | Dong, David, Nagasubramanian, Koushik, Wang, Ruidong, Frei, Ursula K., Jubery, Talukder Z., Lübberstedt, Thomas, Ganapathysubramanian, Baskar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140504/ https://www.ncbi.nlm.nih.gov/pubmed/37123832 http://dx.doi.org/10.3389/fpls.2023.1108355 |
Ejemplares similares
-
Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel
por: Chiteri, Kevin O., et al.
Publicado: (2022) -
“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies
por: Young, Therin J., et al.
Publicado: (2023) -
A farm-level precision land management framework based on integer programming
por: Li, Qi, et al.
Publicado: (2017) -
Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity
por: Jubery, Talukder Z., et al.
Publicado: (2017) -
Genome-wide association analysis of seedling root development in maize (Zea mays L.)
por: Pace, Jordon, et al.
Publicado: (2015)