Agriculture

Many fields, orchards and grazing blocks sit beyond reliable mobile coverage, and a system that assumes a constant cloud connection fails exactly where it is needed. ColdAI's agriculture work uses edge computing for remote field environments with limited connectivity¹, which in practice means deciding up front what must run locally and what can wait for a sync.

Anything time-critical or safety-related belongs at the edge: an irrigation controller that keeps following its last schedule if the link drops, a camera that filters images on the device and sends only flagged frames, a field app that records plot data, photos and signatures offline. Heavy work such as retraining models, processing full drone mosaics or reconciling a season of records runs centrally once data arrives.

Low-power radio such as LoRaWAN suits small, periodic sensor readings over long distances; cellular or satellite backhaul suits images and bulk uploads. The hard questions are mundane: battery and solar budgets, conflicting edits from two offline devices, and how a technician learns that a sensor has gone quiet. Our edge AI and IoT practice covers the device side.

A soil moisture probe on its own says how wet the soil is at one spot. A schedule needs a water balance: how much water the crop is using, how much the root zone can hold, and how much rain and irrigation have added. The widely used reference method is the FAO Penman-Monteith approach to reference evapotranspiration combined with crop coefficients that vary by growth stage, set out in FAO Irrigation and Drainage Paper No. 56².

AI adds value at the edges of that method: forecasting evapotranspiration from weather forecasts, learning how each block's probes respond after irrigation, spotting faulty probes, and planning pump runs around energy tariffs or water allocations. An optimizer that ignores pump capacity, pressure zones or an abstraction licence produces schedules nobody can follow.

Variable-rate application starts with zones, not pixels. Lab results from grid or zone sampling are combined with layers that vary smoothly across a field, such as soil conductivity, elevation and past yield, to draw a few management zones an operator can understand. The output is a prescription map sent to the tractor or spreader, typically through the ISOBUS task controller defined in ISO 11783³.

Nutrient models also have to respect regulation. In the EU, Council Directive 91/676/EEC (the Nitrates Directive) requires action programmes in nitrate vulnerable zones, including closed periods for certain fertilizers and, by default, a cap on livestock manure equivalent to 170 kg of nitrogen per hectare per year⁴. A recommendation engine for farms in those zones must encode the national action programme's limits as hard constraints and keep the records an inspector will ask for.

For agribusinesses the crop becomes a logistics and pricing problem the moment it leaves the field. Useful models here combine an organization's own intake, quality and sales history with public data such as the USDA's monthly World Agricultural Supply and Demand Estimates⁵, and they answer narrow questions: how much grain will arrive at each site and when, which stores to fill first, how many trucks to book, and how exposed open positions are if the harvest comes in smaller.

Price forecasts deserve humility: markets already reflect public information, so models help more with scenario ranges and with linking physical volumes to commercial decisions. A field-level yield forecast feeding an intake plan is often the most valuable link; our crop yield prediction guide shows how to build one honestly.

Our agriculture stack is sensor-agnostic by design¹: we integrate the satellite feeds, drones, probes and machinery a farm or agribusiness already has rather than tying it to one vendor's hardware. Work follows the delivery sequence described on this page, beginning with an assessment of existing data and infrastructure and ending with models calibrated on local soil, weather and crop data¹.

Where provenance matters, as with deforestation-free or sustainability claims, records can be anchored to Hedera Consensus Service so later edits are detectable⁶. We are not always the right choice: if an established farm management platform already answers your question, configure it. Bring us in when data must be combined across systems, the model must be custom, or the system must run without a reliable connection.