Computer vision and machine learning for robust phenotyping in genome-wide studies

Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Jiaoping, Naik, Hsiang Sing, Assefa, Teshale, Sarkar, Soumik, Reddy, R. V. Chowda, Singh, Arti, Ganapathysubramanian, Baskar, Singh, Asheesh K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5358742/
https://www.ncbi.nlm.nih.gov/pubmed/28272456
http://dx.doi.org/10.1038/srep44048

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5358742/
https://www.ncbi.nlm.nih.gov/pubmed/28272456
http://dx.doi.org/10.1038/srep44048

Ejemplares similares