Fordel Studios
The Problem
Modern row crop farming operates at a scale that makes field-by-field, plant-by-plant monitoring impractical through human observation. A 5,000-acre corn and soybean operation has too many acres for timely scouting of every field. By the time a problem — disease pressure, pest infestation, nutrient deficiency — is visible to a scout walking the field, the economically optimal treatment window may have passed.
The data is available. Satellites capture multispectral imagery at 3–5 meter resolution every few days. Weather stations provide hyper-local data. Soil sensors report moisture and temperature at depth. Connected equipment records yield maps and application data. The constraint is not data — it is synthesis: someone or something must monitor all of it continuously and surface the actionable decisions before the window closes.
John Deere's See & Spray technology demonstrated that computer vision applied to in-field imagery can identify weeds at the plant level and apply herbicide selectively, reducing herbicide use by up to 77% on labeled crops. Taranis's remote sensing platform identifies disease and pest pressure from aerial and satellite imagery before it is visible from the field edge. The underlying insight is the same: AI can monitor at a resolution and frequency that human scouting cannot match economically.
The Solution
The Agricultural Monitoring & Recommendation Agent continuously monitors configured fields using satellite imagery, weather data, soil sensor readings, and connected equipment data. It identifies developing problems — disease pressure, pest activity, nutrient stress, irrigation needs — and generates field-specific management recommendations before the window for cost-effective intervention closes.
The agent delivers daily field status summaries and exception alerts to the farm manager or agronomist. When satellite imagery shows early-stage disease signatures in a specific field zone, the agent flags it with a recommended scouting route and, once confirmed by the agronomist, generates a treatment recommendation with timing and rate. Variable-rate prescription maps are generated for equipment that supports them.
Agronomists review and approve recommendations. The agent provides the monitoring and pattern recognition; the agronomist applies local knowledge and makes the management decision.
How It's Built
A satellite imagery ingestion pipeline (Python, rasterio, GDAL) processes multispectral imagery from configured sources (Planet, Sentinel-2, Maxar) on arrival. Vegetation index computation and anomaly detection run as batch jobs on a daily schedule. A weather data integration layer pulls from NOAA, DTN, and on-farm weather station APIs. Computer vision models trained on crop stress datasets classify detected anomalies by type and severity. An LLM synthesis layer generates the field status report and recommendation narrative. Prescription map generation produces GeoTIFF outputs in formats accepted by major precision agriculture platforms.