Drone imagery for crop monitoring was annotated with agriculture-specific labels so an AgriTech team could improve pest detection, crop segmentation and yield-estimation models.
The challenge
Farming seasons created sudden spikes in drone imagery volume, and internal teams could not scale annotation fast enough.
The labels needed agriculture context because generic annotation rules were not precise enough for crop, pest and vegetation-health use cases.
The approach
SBL built an annotation workflow around polygon crop segmentation, pest bounding boxes and NDVI-informed labeling rules.
Quality guidelines, sample reviews and team ramp-up controls helped keep labels consistent as data volume changed.
Map
Define crop, pest and vegetation-health classes against model requirements.
Label
Apply polygon segmentation, bounding boxes and image-level checks.
Scale
Ramp annotation capacity during seasonal spikes while preserving review rules.
The result
The AgriTech team gained a flexible annotation partner that could absorb seasonal data volume while improving training-data quality.
- Crop and pest labels became more consistent across drone imagery batches.
- The client avoided building a permanent in-house team for seasonal peaks.
- Model training received cleaner inputs for crop-health analysis.
- The delivery team could expand as imagery volume increased.