Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator.

ABSTRACT: The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmen

SUBMITTER: Yuan J 

PROVIDER: S-EPMC9394117 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature