ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States. Issue 12 (9th October 2020)

• Record Type: Journal Article
• Authors: Hüther, Patrick; Schandry, Niklas; Jandrasits, Katharina; Bezrukov, Ilja; Becker, Claude
• Is Part Of: The Plant Cell. Volume 32:Issue 12(2020)
• Page Start: 3674
• Page End: 3688
• Publication Date: 2020-10-09
• DOI: 10.1105/tpc.20.00318
• Languages: English
• ISSNs: 1040-4651

Abstract

Abstract : ARADEEPOPSIS is a robust, open-source, and easy-to-use pipeline to extract plant phenotypic traits and to classify leaf states from large-scale image data. Abstract: Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis ( Arabidopsis thaliana ) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.