A real-time phenotyping framework using machine learning for plant stress severity rating in soybean.

ABSTRACT:

Background

Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be c

SUBMITTER: Naik HS 

PROVIDER: S-EPMC5385078 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature