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Published in Methods in Ecology and Evolution

Theia

Edge-AI Geometric Morphometry on Device

A free, open-source edge-AI app for Android. It detects the specimen, predicts the landmarks, routes them through human-in-the-loop validation, and runs GPA and PCA on-device. Offline, with no cloud infrastructure.

Five Erysimum flowers, from capture to PCA. 32 seconds, no sound.

How it works

  1. Image acquisition

    Capture top-down images in the field, or process a gallery of existing photos in Batch Mode.

  2. Automated landmark detection

    A dual-stage pipeline: an object detector locates the flower calyx and a pose-estimation model predicts 32 landmarks. Both run on-device.

  3. Human-in-the-loop validation

    Landmarks are colour-coded by the model's confidence score, so uncertain ones stand out. Pinch to zoom and adjust any coordinate.

  4. On-device morphometric engine

    Generalized Procrustes analysis (GPA) and PCA run offline: morphospace, deformation wireframes, and FAIR-compliant export (a .csv for R and a .json ledger).

Developed and tested on an OPPO Reno4 Z 5G. The YOLOv8-Nano and Medium models were chosen for on-device inference on affordable, older-generation Android devices.

Inside the app

Theia screen: 32 landmarks predicted by the pose model on a flowerTheia screen: a specimen's landmark configuration over the population's mean consensus shapeTheia screen: morphospace scatterplot, PC1 against PC2Theia screen: deformation wireframes for each principal componentTheia screen: table of PCA scoresTheia screen: in situ interpretation field and export of the .csv and .json files

32 predicted landmarks on one flower

Validated against geomorph and experts

Algorithmic fidelity against the geomorph R package, and predictive performance against expert manual digitization, using raw model predictions with no human editing.

|r| > 0.999Algorithmic fidelity: the native engine's GPA and PCA against the geomorph R package, on 157 specimens.
0.92 to 0.98Correlation between AI and manual configurations on centroid size and the first three principal components, on 268 held-out specimens.
92.9% vs. 0.5%Procrustes ANOVA: share of total shape variance due to biological variation among individuals, against the method effect (AI vs. manual).

Coming next

Theia 2.0

Theia 2.0 is in development. The app loads model packs, so the same workflow runs on organisms other than Erysimum flowers. A pack holds the detection and pose-estimation models and the landmark scheme for one structure. You install it on the phone from a file, and the app checks it with a self-test before it uses it.

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Apis mellifera, forewing, 19 landmarks

The annotator

A browser tool to digitize landmarks on your photographs. The photographs stay on your computer: the page reads them from a folder and uploads nothing. When you are done, you export a landmark file.

  • Write your landmark scheme, or click the landmarks in order on the first photograph.
  • Once one photograph is finished, each new one opens with the landmarks proposed from the finished ones. You drag them into place.
  • Zoom, pan and a magnifier for exact placement, keyboard shortcuts, undo and redo.
  • Mark a landmark as missing, or place several specimens on one photograph.
  • Your work is saved in the browser as you go.
  • Opens and writes TPS, NTS, COCO keypoints, YOLO pose and the CSV of the Theia app.

From your photographs to a model pack

A web-based training pipeline turns annotated photographs into a taxon-specific model for the app, in four steps.

  1. Conditioning

    Your photographs and landmarks are checked for duplicates, landmarks outside the frame and shapes far from the average, which often means two landmarks were swapped. Whole groups, such as populations, are held out for validation. You look at everything before training starts.

  2. Training

    An object-detection model learns to locate the structure in the photograph. A pose-estimation model learns to predict your landmarks on it.

  3. Validation

    The model predicts the landmarks on the held-out photographs. GPA, PCA and a Procrustes ANOVA run on your manual landmarks and on the model's, and the report says whether both lead to the same biological conclusions, as in the paper.

  4. Model pack

    Both models, the landmark scheme and a self-test go into one file that you install in the Theia app. From then on, inference runs on-device and offline, in the field.

What you would need

  • Photographs with one specimen or structure each, all taken from the same orthogonal view.
  • The same landmarks on every photograph, in the same order.
  • A group for each photograph, such as population or collecting site.
  • A few hundred annotated photographs.

A model for your organism

Tell us which taxon you would like Theia to work with. We will write to you about Theia 2.0.

We only use your email to reply about Theia.

Paper, app, code and data

Android only. Write to us from your email address and we will reply with the app.

Authors

Cristobal Bragagnolo, Andrés Ferreira-Rodríguez, Mohamed Abdelaziz, A. Jesús Muñoz-Pajares

BioChange Network, Department of Genetics, University of Granada, Spain

How to cite

Bragagnolo, C., Ferreira-Rodríguez, A., Abdelaziz, M., & Muñoz-Pajares, A. J. (2026). Theia: An end-to-end edge-AI mobile framework for high-throughput field phenotyping and on-device geometric morphometrics. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.70416