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.

将植物表型与基因型联系起来是植物育种家和遗传学家的共同目标。然而，收集大量植物的表型数据仍然是一个瓶颈。植物表型分析主要基于图像，因此需要从图像数据中快速、稳健地提取表型测量值。然而，由于分割工具通常依赖于颜色信息，因此它们对背景或植物颜色偏差很敏感。我们开发了一种多功能、完全开源的管道，以无监督的方式从植物图像中提取表型测量值。 ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) 使用顶视图图像的语义分割将叶子组织分为三类：健康、富含花青素和衰老。这使得它在不同发育阶段、叶色和/或表型异常的突变体以及在应激条件下生长的植物的定量表型分析中特别强大。在一组 210 个天然拟南芥 ( Arabidopsis thaliana ) 种质中，我们不仅能够准确分割表型多样化基因型的图像，还能在全基因组关联分析中识别与花青素产生和早期坏死相关的已知基因座。我们的管道准确地处理了不同来源、质量和背景成分以及远缘十字花科的图像。 ARADEEPOPSIS 可部署在大多数操作系统和高性能计算环境上，并且可以独立于生物信息学专业知识和资源而使用。