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.
Capture top-down images in the field, or process a gallery of existing photos in Batch Mode.
A dual-stage pipeline: an object detector locates the flower calyx and a pose-estimation model predicts 32 landmarks. Both run on-device.
Landmarks are colour-coded by the model's confidence score, so uncertain ones stand out. Pinch to zoom and adjust any coordinate.
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.






32 predicted landmarks on one flower
Algorithmic fidelity against the geomorph R package, and predictive performance against expert manual digitization, using raw model predictions with no human editing.
Coming next
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.
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.
A web-based training pipeline turns annotated photographs into a taxon-specific model for the app, in four steps.
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.
An object-detection model learns to locate the structure in the photograph. A pose-estimation model learns to predict your landmarks on it.
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.
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.
Tell us which taxon you would like Theia to work with. We will write to you about Theia 2.0.
Android only. Write to us from your email address and we will reply with the app.
Cristobal Bragagnolo, Andrés Ferreira-Rodríguez, Mohamed Abdelaziz, A. Jesús Muñoz-Pajares
BioChange Network, Department of Genetics, University of Granada, Spain
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