πŸ›°οΈ AstroRoot

Turn a photo of seedling roots into numbers β€” in your browser, nothing uploaded.

Code & data (GitHub) Tutorial
classical baseline ready
not calibrated (results in px)

Train your own model

The neural network can't be trained inside the browser β€” that needs a GPU. So AstroRoot splits the job: you label examples here, train in the cloud (a free Colab or CyVerse notebook), then load the finished .onnx back into AstroRoot.

  1. Label roots. Load a photo, then trace each root by clicking along it (click to add points, double-click to finish a root). Each trace is saved as an RSML polyline.
    0 roots traced
  2. Build the training set. Add each labelled image to your dataset, then export it as a zip of image + RSML pairs (the exact format RootNav 2.0 trains on).
    0 images in dataset
  3. Train in the cloud. Open the training notebook, upload your zip, run it (β‰ˆ30–60 min on a free GPU). It fine-tunes the Arabidopsis model and gives you a model.onnx.
  4. Use it. Back on the Single/Batch tabs, choose My own model (.onnx)… and pick your file. It now traces roots in your style.

About AstroRoot

A free, install-free root-image analyser for students and teachers. It brings together the friendly, correct-by-hand feel of RootNav 1 with the automatic tracing of RootNav 2.0's deep-learning model β€” running entirely in your browser, so student photos never leave the device.

New here? The hands-on tutorial walks through every feature. Start with the Synthetic β€” clean, known answer demo roots in the picker above: a simple shape drawn at a known scale, so the measurements can be checked against an exact expected value (unlike the real, deliberately diverse NASA/ABRS plates).

Built on RootNav 2.0 and RootNav 1 (both BSD-3-Clause, University of Nottingham). Part of the AIRI astrobotany program.