AGRICULTURE
Field Boundary Segmentation
Field Boundary Segmentation. What it takes to run it.
Precise mapping of plantable areas vs. obstacles and pathways.
// 01 · The question
“Which of this ground is actually plantable?”
// 02 · Why it is hard
Boundaries are agreed, not observed. A grass margin, a turning headland and an unplanted wet patch are all not-crop, and which of them counts as plantable is a business rule rather than a visual fact.
// 03 · What the labels have to be
Polygon
Decided before the first frame
The output is an area figure and a map that machinery follows, both of which are polygons downstream in the farm management system. Labelling in the same geometry the consumer expects avoids a lossy conversion.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Field Precision
Annotate crops, equipment, obstacles, and pathways with polygons, polylines, and semantic segmentation. Tag environmental factors like dust, shadows, and occlusions. 80k+ image annotations in production deployments.
LexInsight
Yield Intelligence
Transform plant-level detections into field-level intelligence. Map disease patterns across zones, track crop health progression over time, estimate yield from visual data.
LexAlert
Navigation Alerts
Monitor autonomous navigation models against seasonal drift. Retrain as fields change to keep navigation reliable through the seasons.
// 05 · What breaks it after launch
Cloud shadow, standing water and low winter sun all move where the boundary appears to be. The field has not changed shape; the imagery has, and the acreage figure moves with it.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In agriculture
GPS-Denied Autonomy
The Challenge
Unreliable GPS under tree canopies and low visibility. Models failed in unstructured, obstacle-heavy terrains.
Our Execution
Annotated thousands of images with polygons and polylines. Tagged diverse environmental factors like dust, shadows, and occlusions.
The Result
Enhanced autonomy in GPS-denied environments. Faster deployment of AI-powered navigation.
80k+
Image annotations in production