Segment Any Plant (SAP): Foundation-Model Segmentation for Plant Time-Series Phenotyping

Clicks: 9
ID: 329965
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #325 of 343 articles by views in Journal of experimental botany

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 343 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape, growth, and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy on macroscopic organs (mean IoU 0.89-0.93) and pixel-level centerline precision from single-frame prompting, with lower but promising accuracy at the cellular scale (confocal, mean IoU 0.75) that we report as a proof of concept. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Reference Key
openalex_W7135191814 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Alex Abbey, Yasmine Meroz
Journal Journal of experimental botany
Year 2026
DOI
10.1093/jxb/erag467
URL
https://doi.org/10.1093/jxb/erag467
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.