Computer vision for bracken detection
Investigate visual detection methods and assess how they behave across relevant field conditions.
Environmental data intelligence
Bracken detection and mapping
We contribute computer vision and data processing to investigate bracken detection and mapping within a collaborative rover project.
Project context
The project and 42able’s role
Bracken / GreenSquirrel investigates how to identify bracken in field images and map observations as part of a collaborative autonomous rover project. It combines environmental monitoring, robotics and data intelligence.
42able contributes computer vision, image and spatial data processing, geotagging, mapping and obstacle detection research, with testing, validation and integration support. The wider collaboration is responsible for developing the complete rover.
- Area
- Research
- Category
- Environmental data intelligence
- Current status
- Active applied R&D
- Stage
- Experimental systems · testing and integration
Image and spatial evidence
Images and location data
42able’s contribution connects visual observations with spatial context. Detection quality and location uncertainty both affect how the resulting information can be used.
- Field images
- Image data used to investigate bracken detection under changing vegetation, lighting and viewing conditions.
- Spatial context
- Location information associated with observations, retained alongside the visual evidence and its uncertainty.
- Detection outputs
- Candidate detections to review against the source images, including incorrect detections and observations that methods may miss.
- Mapped observations
- Geotagged observations brought into a geographic view for inspection and spatial analysis; a location record alone does not establish an object’s exact position.
Technical responsibilities
42able’s contribution
Our technical responsibilities cover the following components and support for their integration with the wider rover system.
Image and spatial data processing
Prepare and connect image and spatial information while preserving the context needed to inspect an observation.
Detection geotagging
Associate detections with location evidence and retain the uncertainty involved in that association.
Geospatial mapping and analysis
Bring observations into mapped views for inspection, comparison and analysis of their spatial relationships.
Obstacle detection research
Investigate methods for detecting obstacles that can contribute to the wider system’s understanding of its surroundings.
Testing, validation and integration support
Support evaluation of the AI and data components and their integration with the collaborative rover system.
Current status
Research status and evidence
Applied research and development is ongoing. 42able’s methods and components remain experimental. Testing and validation must assess detection quality, field reliability and integrated system behaviour in conditions relevant to the wider project.
Detection benchmarks, validated field results and integrated rover performance have not been published here.
Research questions
What needs validation?
- How do bracken and obstacle detection methods behave as lighting, vegetation, terrain and viewing conditions change?
- How can missed and incorrect detections be assessed using relevant field observations?
- How accurately can detections be associated with location, and how should that uncertainty be represented on a map?
- What testing is needed to understand the behaviour of these components when integrated with the rover?
Work with us
Discuss a research collaboration
Contact us to discuss how our data analysis, machine learning or computer vision expertise could contribute to your research project.