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USE CASES / VISION INTELLIGENCE

Earth observation + computer vision + agents

From canopy
to kernel.

Detect landscape change, preserve EUDR evidence, count biological assets and measure physical traits, then let OrbitAI route verified results into decisions and workflows.

One intelligence continuum

Observe the system at the resolution the decision requires.

Cropin combines satellite, high-resolution aerial, drone and close-range imagery with domain models. The output can move from landscape change to individual-object detection without separating the evidence from its place and time.

01Landscape

Forest cover and land-use change

02Asset

Tree crown, height and biomass context

03Organism

Plant and fruit detection

04Trait

Leaf dimensions, shade and kernel count

Deforestation and land-use evidence

Detect change. Keep the evidence attached.

Visual intelligence should not stop at a colored change layer. Cropin connects the detected event with the plot boundary, before-and-after imagery, observation window and human review, creating a defensible record for EUDR and other land-use workflows.

Cropin Cloud human confirmation workflow with before-and-after satellite evidence for a detected deforestation event
BEFORE + AFTER · SOURCE EVIDENCEHUMAN-SUPERVISED CONFIRMATION
SCREEN

Find plots that need review

Rank sourcing areas by detected land-use change and focus attention where evidence is material.

INSPECT

Compare the source evidence

Keep plot boundaries, observation dates and before-and-after imagery together for investigation.

CONFIRM

Preserve human judgment

Require a reviewer to confirm or reject consequential events and retain the supporting record.

Explore the complete EUDR evidence workflow ↗

Food & agriculture vision models

Count and characterize what is physically present.

Vision models can support crop scouting, phenotyping and operational sampling by detecting plants and fruits, estimating visible canopy characteristics and counting kernels from field or close-range imagery.

Vision model output detecting fruits across a field
PLANT + FRUIT COUNTINGField-level object detection
Vision model output counting kernels on a maize cob
KERNEL COUNTINGSpecimen-level measurement
Vision model output identifying leaves and measuring leaf dimensions
PHENOTYPINGLeaf shape, dimensions and canopy area

Phenotyping experiments in the supplied study used 240 in-house images spanning five shade categories under five illumination conditions. Counts shown in the imagery are example model outputs, not accuracy or performance claims.

Operationalized through OrbitAI

The agent does not replace the model. It makes the model useful.

OrbitAI can select the relevant imagery and model, compare the result with historical and enterprise context, identify exceptions, request human verification and prepare the next action.

01Ask

“Where did canopy decline?”

02Invoke

Run the appropriate vision and domain models.

03Verify

Check confidence, history and field evidence.

04Recommend

Prioritize inspection or intervention.

05Act

Update the approved enterprise workflow.

CAPABILITY EVIDENCE FROM CROPIN STUDIES · DEPLOYMENT DESIGN, SENSOR AVAILABILITY, CALIBRATION AND VALIDATION DEPEND ON THE ASSET, GEOGRAPHY AND DECISION · PROPRIETARY MODEL IMPLEMENTATION IS NOT DISCLOSED

Bring us the asset and decision

Make every object
observable and actionable.

Design a vision workflow ↗