Models that run in the field
Train on the images your equipment already captures, then deploy to hardware that runs the model locally instead of depending on a connection.
- Runs without a connection
- NanoVision centroid detection
- Raspberry Pi and UNO Q
What this solves
Field sites rarely have reliable connectivity, and sending video back for processing is not an option. You bring the imagery; the model runs on the device that captured it.
Who this is for
- Agritech engineering teams
- Equipment manufacturers
- Research and agronomy teams
Why edge inference fits
Decisions happen locally
Inference runs on the device itself. There is no round trip to a server between a frame and a result.
Keeps working offline
A deployed model runs without a connection. Devices send heartbeats and inference logs when they reconnect.
Data stays where it is captured
Frames are processed on the device. Nothing has to leave the site for a prediction to be made.
The whole pipeline, one stage at a time
Every stage below is a screen in the platform, not a diagram of something you still have to build.
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01Stage 1 of 10
Collect data
Upload samples or import an existing folder tree.
Data
What you get in the platform
Dataset upload and import
Upload samples, import a folder tree, or map columns with the CSV wizard.
Bounding-box annotation editor
Draw, adjust and relabel boxes directly on the dataset grid.
NanoVision and Vision Pro detection
Centroid detection with NanoVision v1 or v2, or box detection with Vision Pro.
Live classification
Send a frame or a device stream through the current model and see the prediction immediately.
Deployment builds
Turn any trained model into a ready-to-run package for the target you pick.
Device fleet
Register devices with an API key, then track heartbeats and inference logs per project.
What teams build with it
Crop versus weed classification
Classify what the camera sees as it passes over a row.
Counting with centroids
Many small objects per frame, without paying for boxes.
Pest presence detection
Detect whether a target is present in a captured frame.
Livestock counting
Count animals in a frame and hold the identities across frames.
Grading harvested produce
Sort what passes the camera into the grades you already use.
Where this runs
The targets teams reach for on this kind of project.
Raspberry Pi
Python inference package
Installs with the device client and runs as a service
UNO Q
Linux runtime package
The on-device runner takes it from there, no glue code to write
TensorFlow Lite
TensorFlow Lite model with an inference library
Embed it in any runtime that can load TensorFlow Lite
Petal Edge builds for six targets in total, each with its own package format and constraints.
See all deployment optionsOne platform, start to finish
Data, labels, training, evaluation, post-processing, deployment and the fleet live in one place, with nothing to stitch together.
The whole lifecycle
Collect, label, train, evaluate, post-process, deploy and monitor without leaving the platform.
Models sized for edge hardware
NanoVision, Vision Pro and MobileNetV2 are built to run on devices, not on datacentre GPUs.
Six deployment targets
One trained model, packaged for the hardware you already run.
A fleet you can reach
Register devices, read their inference logs, and push new models over the air.
What’s included
- Dataset import from COCO, YOLO, Pascal VOC, Open Images and Edge Impulse JSON
- AI-assisted labeling with human review before anything is applied
- Confusion matrix, per-class metrics and model testing across versions
- Post-processing with NMS and IoU tracking
- Over-the-air model updates to registered devices
- Project export, so your data and models stay portable
Train on your field imagery
Create a project, bring your data, and take it through to a deployed model.