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Their adaptive function places them as an important target for breeding environmentally resilient plant species. Quantifying these structures at high resolution is a manual bottleneck that limits experimental scale. We present RADIX (Root Anatomy Deep- learning Image segmentation across species and platforms), a framework that adapts a large self-supervised vision-transformer foundation encoder (DINOv3), pre-trained on billions of natural images, to root anatomy by fine-tuning its encoder with a dense-prediction-transformer decoder. Transferring these general-purpose vision encoders to a specialized biological domain with a high-quality annotated dataset is what allows RADIX to generalize across species and imaging platforms. We train and evaluate it on the first expert-annotated benchmark of root anatomical structures at scale, comprising 1,695 high-quality fluorescence images spanning 17 monocot and dicot species, six anatomical structures, and three imaging platforms. RADIX segments all six structures at inter-annotator-level accuracy and generalizes to unseen species, genotypes, growth conditions, and an imaging platform from an independent laboratory. A single unified model surpasses monocot- and dicot-specialist models without sacrificing in-group accuracy. Predicted masks yield aerenchyma and suberin\/lignin measurements matching expert annotation at \u223c1.2 s per image with a single GPU, reducing weeks of manual analysis to minutes. Applying RADIX across genotypes, microbial treatments, and growth systems, we show that these cell type features form a coordinated, multidimensional, and context-dependent system shaped by genetic and environmental factors.<\/jats:p>","DOI":"10.64898\/2026.08.07.743584","type":"posted-content","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T12:40:23Z","timestamp":1786365623000},"source":"Crossref","is-referenced-by-count":0,"title":["RADIX: a deep learning framework that maps root barriers across species and reveals genetic and environmental contributions"],"prefix":"10.64898","author":[{"given":"Yifei","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of California, Davis, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Sanow","sequence":"additional","affiliation":[{"name":"Department of Plant Biology and Genome Center, University of California, Davis, CA, 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Adeel","family":"Zafar","sequence":"additional","affiliation":[{"name":"Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas","family":"DeMello","sequence":"additional","affiliation":[{"name":"Department of Plant Biology and Genome Center, University of California, Davis, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California, Davis, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Knab","sequence":"additional","affiliation":[{"name":"Department of Plant Biology and Genome Center, University of California, Davis, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Busch 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