Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remains 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. ᴀʀᴀᴅᴇᴇᴘᴏᴘsɪs (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 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 GWA analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly-related Brassicaceae. ᴀʀᴀᴅᴇᴇᴘᴏᴘsɪs is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.
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