Agricultural AI
Computer vision and data intelligence for understanding crops, field conditions, and agricultural operations.
UNITONE AI / 03
Agricultural R&D
Researching AI and autonomous systems for agriculture.
Research and development in agricultural AI, connected sensors, robotics, and autonomous vehicles. Our focus spans crop observation, field data, navigation, and machine control.
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AGRICULTURAL INTELLIGENCEAGRICULTURAL RESEARCH & DEVELOPMENT
From field observations to decisions and machine control.
Our agricultural R&D focuses on the connection between software and field equipment. We explore how cameras and sensors can capture field conditions, how AI can help interpret those observations, and how navigation and control systems can support agricultural vehicles and robotics.
Where we focus.
AGRICULTURAL R&DAutonomous vehicles
Navigation, perception, and control systems for agricultural mobility and task-oriented field machines.
Robotics & sensing
Connected sensors, embedded software, and robotic systems that link field observations with useful actions.
Precision operations
Decision-support software and integrated systems for planning, monitoring, and managing agricultural work.
RESEARCH & DEVELOPMENT
Research questions rooted in field work.
Our agricultural work explores concepts and engineering feasibility. Designs need evaluation against crop requirements, field conditions, operator needs, and the practical costs of maintenance.
Crop observation & field records
Explore camera-assisted observations and sensor readings that help operators review crop conditions and changes over time.
Evaluate data quality across crops, seasons, lighting, and camera positions; retain agronomic review.Agricultural implements & tools
Investigate task-specific tool concepts, mechanical layouts, and interfaces between implements, sensors, and controls.
Evaluate loads, materials, serviceability, manufacturability, and safe operator interaction.Assisted mobility & autonomy
Explore perception, route planning, and machine control for bounded agricultural tasks, with operator supervision.
Evaluate obstacle handling, localisation, loss of connectivity, intervention, and safe stopping before field use.Technology under consideration.
Tools are selected for each research question. These are candidate approaches, with adoption dependent on evaluation.
- Mechanical exploration
- CAD, basic structural analysis, and prototype measurements can help compare implement geometry, mounting, and material choices.
- Perception & simulation
- Python, OpenCV, machine-learning frameworks, and robotics simulation are candidate tools for evaluating vision and control concepts.
- Connected field systems
- Embedded controllers, GNSS, cameras, and environmental sensors can be evaluated alongside edge processing and telemetry.
How a concept moves forward.
Define the problem and constraints → build a focused prototype → evaluate against agreed criteria → refine or reconsider. Each stage has defined evaluation criteria, with product readiness and field suitability assessed through testing and review.
FIELD ENGINEERING
Agricultural systems have to account for changing conditions.
Crop type, terrain, light, connectivity, and operator needs influence the design. These requirements determine which sensing, software, and control approaches are worth evaluating.
- Observation
- Camera and sensor inputs, data quality, and changes in weather or lighting.
- Navigation & control
- Positioning, terrain, obstacles, and the behaviour of the machine being controlled.
- Operator oversight
- Clear status, intervention controls, and a safe response to uncertainty.
- Field validation
- Testing around the intended task and operating conditions, with defined evaluation criteria.
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Bring us a field requirement.
Tell us about the crop, operation, equipment, or data problem you want to explore.