Health & Life Sciences
Agriculture
In agriculture, data comes from the field and conditions are never controlled. The model has to keep working as season, light and region change.
Problems we address in this sector
- Early detection of disease and pests; diagnosis from imagery
- Yield forecasting and harvest timing
- Grounding irrigation and fertilisation decisions in data
- Maintaining model stability across regions and seasons
Approaches we apply
- Computer VisionObject detection and tracking, video understanding, vision-language models, document intelligence and OCR, real-time inference on edge devices.
- MLOps & Model OperationsEvaluation infrastructure, observability, hallucination and regression tracking, continuous retraining.
- Reinforcement Learning & Decision SystemsDecision policies that adapt to changing conditions, reward design, training in simulation and safe transfer to the field.
Other sectors in this group
Do you have a problem in this sector?
Let's define the problem together in a technical consultation; feasibility is assessed at the first stage.