Ground truth is expensive
Verifying a pest or disease label often means an agronomist in a field. That cost shapes what a dataset can realistically contain.
Built for teams growing yield with models
AI Training Data for Agriculture and AgriTech
Crop health, pest pressure and yield forecasting depend on data that reflects real fields — variable light, mixed growth stages and messy ground truth.
From Field Capture to Field Decisions
Agricultural models fail in the gap between a research dataset and a real farm. We build training data from the conditions your models will actually meet: uneven canopies, changing light, mixed varieties, and the seasonality that makes last year's dataset only half useful this year.
Where Agriculture & AgriTech AI Programmes Struggle
Seasonality resets the problem
A model trained on one growth stage degrades against the next. Datasets need a calendar, not just a size.
Visual classes overlap
Nutrient deficiency, early blight and water stress look alike to a camera and to an untrained labeller.
Capture is inconsistent
Handheld phones, drones and fixed cameras produce very different images of the same problem.
AI Use Cases We Support
Crop and weed segmentation
Pixel-level separation for precision spraying and mechanical weeding.
Pest and disease detection
Labelled lesions and infestations with agronomist-reviewed classes.
Yield estimation
Fruit and grain counting from imagery, calibrated against harvest records.
Growth-stage classification
Phenology labelling for irrigation and input scheduling models.
Soil and field mapping
Segmentation of soil condition and field boundaries from aerial capture.
Livestock monitoring
Animal detection, counting and behaviour labelling from barn and pasture cameras.
Data Types We Handle
- Field and drone imagery RGB and multispectral capture, segmented and classified.
- Handheld photography Farmer-captured images labelled with the variability they really contain.
- Sensor and weather series Soil moisture, temperature and rainfall annotated for events.
- Agronomy records Advisory notes and treatment logs structured for supervised learning.
How We Run the Programme
- Agronomist-reviewed taxonomies Classes are defined with someone who has diagnosed the problem in a field.
- Seasonal batching Delivery is planned around the crop calendar so coverage is real, not incidental.
- Confidence on hard classes Look-alike conditions are double-labelled and disagreements are escalated, not averaged.
- Feedback from the field Model errors reported by users flow back into the next labelling round.
Build Agriculture & AgriTech AI on Data You Can Trust
Tell us what you are building and we will come back with a plan, a sample and a price.