Drone Imagery Crop Analytics for an AgriTech Startup

The Challenge

The promise was solid; the delivery was manual. Agronomists were downloading raw multispectral imagery, stitching orthomosaics by hand in desktop GIS tools, and eyeballing NDVI maps to flag problem areas. Producing one field report took days — often long enough for a nitrogen deficiency or fungal outbreak to spread past the point where intervention was cheap.

The founders needed a real product: an automated pipeline that could ingest imagery at scale, detect and classify crop stress, and put results in front of agronomists within hours of a flight. They also needed it before the next growing season and the next funding round — and they had no machine-learning engineers on staff.

What We Did

Dillo scoped the MVP as a fixed-bid outsourcing engagement with an AI-focused delivery team: two ML engineers, a geospatial data engineer, a full-stack engineer, and a part-time delivery lead. The client's agronomists acted as domain experts and labelers throughout.

• Built an automated ingestion pipeline on GCP that accepts drone flight uploads and scheduled satellite pulls, normalizes them with GDAL, and tiles imagery for processing — no more manual stitching.
• Trained PyTorch segmentation and classification models to detect crop-stress signatures — water stress, nutrient deficiency, pest and disease pressure — from multispectral bands, using a labeling workflow the client's agronomists ran themselves.
• Combined model output with vegetation-index analytics (NDVI, NDRE) so agronomists could see both the model's call and the underlying signal.
• Delivered a React agronomist dashboard with field-level heatmaps, stress-zone boundaries, season-over-season comparisons, and one-click grower-facing PDF reports.
• Set up model-monitoring and retraining loops so agronomist corrections feed back into training data each season.
• Ran two rounds of field trials with pilot growers, validating detections against ground-truth scouting before the commercial launch.

The Results

• In field trials, the pipeline flagged 87% of crop-stress events earlier than the growers' regular scouting rounds caught them.
• The platform analyzed more than 50,000 acres in its first full growing season.
• Field-report turnaround dropped from days to hours — imagery uploaded in the morning is an annotated report by the afternoon.
• The MVP shipped in four months, in time for planting season and the client's Series A conversations.
• The client's agronomists now spend their time interpreting flagged zones and advising growers, not stitching imagery.

Figures reflect outcomes reported for this engagement; they are project results, not audited benchmarks.

Tech Stack

Services Used

Software Outsourcing (fixed-bid AI/ML product build with an embedded computer-vision team).