Apr 8, 2020

ARADEEPOPSIS: From images to phenotypic traits using deep transfer learning

BioRxiv : the Preprint Server for Biology
Rodney J DyerClaude Becker

Abstract

Linking plant phenotype to genotype, i.e., identifying genetic determinants of phenotypic traits, is the goal of plant breeders and geneticists alike. While the ever-growing genomic resources and the rapid decrease of sequencing costs have greatly facilitated obtaining the critical amount of genomic data, collecting phenotypic data for large numbers of plants remains a bottleneck. Many phenotyping strategies rely on recording images of plants, which makes it necessary to extract phenotypic measurements from these images in a fast and robust way. Common image segmentation tools for plant phenotyping mostly rely on color information, which is error-prone when background or plant color are variable in the experiment or deviate from the underlying expectations. We have developed araDEEPopsis, a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. araDEEPopsis was built around the deep-learning model DeepLabV3+ and re-trained for segmentation of Arabidopsis thaliana rosettes. It uses semantic segmentation to classify leaf tissue into up to three categories: healthy, anthocyanin-rich, and senescent. This makes araDEEPopsis particularly powerful at quantitative phenotypi...Continue Reading

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Mentioned in this Paper

Anatomic Bifurcation
Cell Differentiation Process
Objective (Goal)
Genetic Studies
Population Group
Biological Evolution
Landscapes
Stem Cells

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