Starts with location
The system begins with latitude and longitude instead of asking a user to manually enter lab-style agronomic values.
gps grounded crop intelligence
The rollout is paused. The product direction is not.
CROP AI is being held back while the financial side gets sorted, but the core idea is already clear: turn a pair of coordinates into a live crop recommendation backed by soil signals, weather context, model prediction, and AI-generated farming guidance.
what it is
A crop recommendation system built for actual field use.
Most crop models stop at benchmark accuracy and still expect manual entry of soil and climate values. CROP AI is designed around a more useful flow: location in, recommendation out.
Pulls live field signals
It gathers soil estimates and weather conditions from live data sources so the prediction is grounded in real conditions, not a blank form.
Explains the result
The final output is not just a crop label. It is meant to read as a practical recommendation with context around why it fits.
how it works
Four linked parts, one decision flow.
CROP AI chains together a live-data pipeline and a prediction stack so the product can move from raw coordinates to a usable farming suggestion in one pass.
1
Soil analyzer
Reads nutrient and pH signals from global soil layers and shapes them into model-ready inputs.
2
Weather analyst
Adds temperature, humidity, and rainfall context so the field is described by more than soil alone.
3
Crop predictor
Runs the agronomic feature set through a trained classifier to identify the most suitable crop direction.
4
Insight engine
Turns the prediction into readable guidance so the system feels closer to a decision partner than a raw model output.
why it matters
Built to be fast enough for product use, not just model demos.
The stack is tuned around real inference speed and deployability, with the model served in ONNX format so the system can stay lean when it eventually ships.
99.32%
Held-out accuracy on the augmented crop recommendation dataset.
GPS IN
The intended product flow begins from coordinates, not manual feature entry.
ONNX
Inference is optimized for lightweight serving and faster startup behavior.
current state
The work is active. The launch is what paused.
Right now the constraint is financial, not conceptual. The system direction, the architecture, and the product intent are intact. This page exists so people can understand what CROP AI is becoming while the project is getting back into position.
Product first
The focus is on turning the research into a clean user-facing experience that feels useful outside a paper or notebook.
Infrastructure minded
The system is being shaped around deployment reality, not just offline evaluation, so it can support a real single-page product.
Meanwhile, this is the idea
CROP AI is about helping a user understand what to grow on a given patch of land with less guesswork and less manual friction.