AI Workflow Automation for Agriculture

AgriTech and FoodTech data is notoriously fragmented, and that is exactly where an AI MVP earns its keep. A working prototype has to reconcile Sentinel-2 and Landsat imagery, LoRaWAN soil-moisture and EC probes, NOAA weather feeds, and machine telematics pulled from the John Deere Operations Center API or Climate FieldView — often over ISOBUS/ISO 11783 from the cab. We have shipped 18+ AI MVPs, and in this vertical the first two weeks are usually spent building the ingestion layer that turns GeoTIFFs, SDI-12 sensor streams, and shapefiles into a single field-and-lot data model your agronomists and models can actually query.

Computer vision is the most common wedge we build first. For growers that means multispectral disease and pest classification — flagging leaf rust, blight, or aphid pressure from DJI Agras or MicaSense captures — and see-and-spray weed detection that drives variable-rate herbicide maps. For packers and processors it means produce grading and defect detection on the line: sizing, color, bruise and foreign-material rejection at belt speed. Because rural upload bandwidth is unreliable, we typically quantize these models to run on-device (Jetson or mobile) and sync inferences when a connection returns, rather than assuming a round trip to the cloud.

The second common build is forecasting. Yield prediction models fuse NDVI/NDRE time series with growing-degree-days and reference evapotranspiration to estimate tonnage weeks before harvest, and the same pipeline produces variable-rate prescriptions exported as shapefiles or ISO-XML that a rate controller can actually read. On the input-cost side we forecast irrigation demand from soil-moisture curves and short-range weather, so a 2-3 week MVP can already show a grower a defensible number instead of a gut call. We keep the training loop transparent so your agronomy team can challenge and correct the model rather than treating it as a black box.

On the FoodTech side the economics are about shrink and spoilage. We build demand-forecasting and dynamic-replenishment models for grocers, ghost kitchens, and meal-kit operations that cut over-ordering on perishables, plus cold-chain anomaly detection that scores temperature-logger and telematics data against HACCP critical limits before a load spoils. This is adjacent work to our logistics and food-delivery builds — the ai-logistics-optimizer case study covers route and load optimization under time and temperature constraints, and the food-delivery-app case study covers the marketplace and dispatch layer a perishable-goods flow sits on top of.