The AI Agronomic Advisor for Field-Specific Decisions

Understands how weather conditions affect your fields - not just today, but over time and across the season. Weather data is updated daily, combined with five years of historical records and forward-looking forecasts. This allows the AI to place current conditions in context and support both immediate and forward planning decisions.

The platform translates raw weather data into field-relevant agronomic indices, including:

All indices are visualized with clear graphs and agronomic scales, helping farmers and agronomists quickly interpret conditions and trends.

Understands the economic context behind every agronomic and operational decision - not only what can be done in the field, but what makes financial sense under current market conditions.

The AI Advisor combines agronomic insights with real-time and historical market data, including commodity prices, futures, exchange rates, and input-related costs. This allows recommendations to reflect both field conditions and economic reality.

By connecting soil, weather, operations, and market signals, the platform helps:
Evaluate decisions against current and forward-looking price environments
Understand how timing, inputs, and operations interact with market conditions
Compare agronomic options based on risk, cost, and potential return
Support planning under price volatility and changing economic conditions

The result is field-specific, economically informed decision-making, grounded in both agronomy and market dynamics.

Understands what actually happens in your fields - how machines are used, when operations are performed, and how those actions drive agronomic outcomes and costs.

By connecting to operational data from leading equipment ecosystems such as John Deere and CNH, the AI Advisor evaluates real field activity across the full crop cycle. This includes seeding operations (rates, spacing, timing), tillage (depth, passes, soil disturbance), applications (fertilizer, crop protection, variable rate execution), and harvesting (yields, efficiency, losses), along with machine usage patterns, fuel consumption, and timing of each pass.

The result is advice grounded in how your farm actually operates - reflecting real decisions, real constraints, and real performance in the field, rather than theoretical assumptions.

Continuously monitors your fields using satellite imagery and vegetation indices to understand crop development, spatial variability, and emerging stress patterns.

Satellite data is updated every few days and combined with five years of historical imagery, allowing the AI to identify trends and compare seasons.

One of the key indicators used is NDVI (Normalized Difference Vegetation Index) - a simple measure derived from satellite images that reflects crop vigor and biomass, helping identify healthy areas as well as zones under stress.

Understands the foundation of every agronomic decision - the physical and chemical soil properties that define variability, nutrient dynamics, and yield potential within each field.

Soil data can come from multiple sources. Terra Oracle AI integrates high-resolution maps generated by the Terra Oracle AI dual-sensor soil analysis, as well as externally provided soil analysis results such as traditional soil sampling, laboratory analyses, EM mapping, Stenon measurements, and other sensing methods.

When soil analysis data is available, the platform leverages dense, field-scale variability maps to maximize spatial resolution and zonal precision. When external data is provided, it is incorporated into the same reasoning framework - ensuring flexibility and consistency.

By combining soil properties with satellite imagery, weather patterns, operational history, and economic signals, the AI Advisor identifies nutrient variability and pH imbalances, supports zonal and variable-rate planning, links soil constraints to crop performance, and strengthens long-term field strategy through historical comparison - delivering soil - informed decisions based on measurable field conditions rather than averages.