Open AccessRootNav 2.0: Deep learning for automatic navigation of complex plant root architectures
Author(s) -
Robail Yasrab,
Jonathan A. Atkinson,
Darren M. Wells,
A. P. French,
Tony Pridmore,
Michael P. Pound
Publication year - 2019
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giz123
Subject(s) - computer science , artificial intelligence , convolutional neural network , pattern recognition (psychology) , deep learning , root (linguistics) , segmentation , feature extraction , feature (linguistics) , computer vision , philosophy , linguistics
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 extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction.