RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

ABSTRACT:

Background

In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an ex

SUBMITTER: Yasrab R 

PROVIDER: S-EPMC6839032 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature