Our AI Agronomic Advisor in the Press: From Fragmented Farm Data to Real-Time Decisions
Our founders on turning fragmented farm data into explainable, multilingual, real-time agronomic decisions - an interview now republished across European and Asian agriculture media.
Terra Oracle AI founders Erez Biton and Yuri Gushin sat down for an interview on a problem every modern grower recognises: farms are drowning in data but starving for answers. The conversation was picked up across the agricultural press and republished in several languages - in English, Dutch, and Ukrainian - reflecting how widely the shift from precision agriculture to AI-guided farming is resonating.
The problem: drowning in data, starving for answers
Soil analysis reports, satellite imagery, weather stations, irrigation systems, scouting reports, agronomic recommendations - they all arrive separately, leaving growers unsure what to do next. As Erez put it in the interview:
“The challenge is no longer data access; it is understanding what is actually happening in the field, why it is happening, and what action should be taken next.”
That gap is especially costly in protected cultivation and high-value crops, where small mistakes in irrigation, nutrient management, salinity, disease pressure, or climate control can carry significant economic consequences.
What we shared: an explainable AI agronomic advisor
The core of the interview was how Terra Oracle AI’s AI agronomic advisor consolidates fragmented data into a single, actionable decision system. The platform combines explainable AI agronomic reasoning, soil intelligence, satellite monitoring (NDVI), weather data, historical field behaviour, machinery operations, and economic analysis - all in one place.
Yuri framed the direction this way:
“We believe agriculture is moving beyond passive dashboards into systems that actively monitor fields, identify risks early and help growers make operational decisions in real time.”
What sets the platform apart, the founders explained, is the agronomic reasoning layer built on top of the data: instead of passive dashboards, it provides proactive agronomic alerts, field-specific recommendations, explainable reasoning, and multilingual conversational interaction.
Two patent-pending technology layers
The interview highlighted Terra Oracle AI’s two patent-pending layers:
- An explainable AI Agronomic Advisor that reasons across multiple data streams together - soil properties, weather, NDVI vegetation indices, irrigation behaviour, topography, field operations, and historical crop performance.
- A dual-sensor soil scanning platform that combines gamma radiation spectroscopy with optical sensing for rapid, large-scale field scanning and high-resolution soil intelligence, without relying solely on traditional manual soil sampling.
Multilingual by design
A recurring theme was language. During demonstrations in Gujarat, India, growers showed strong interest in interacting with the advisor in their own languages while running potato and groundnut demonstrations. The founders noted that the barrier to technology is often language itself - farmers may not be as comfortable in English as in their native tongue.
The fact that this interview itself was republished in English, Dutch, and Ukrainian mirrors exactly what the product is built to do: make agronomic decision support accessible across languages.
Where it is being tested
The technology has been tested across broad-acre farming, irrigated row crops, and specialty crops including potato, tomato, cucumber, onion, and carrot, in both open-field and protected cultivation. Pilot projects and demonstrations have run across Europe and Asia - including India, France, Spain, Slovenia, Romania, Poland, Bulgaria, and Ukraine.
The founders see particularly strong potential in India, Brazil, Thailand, Australia, Eastern Europe, and North America.
Recognition
The coverage also followed Terra Oracle AI’s Applied Technology Trophy win at AGRITECHNICA ASIA 2026 in the Digital & Automation Solutions category - further validation that practical, explainable agricultural AI is moving from demos into the field.
Read the coverage
The interview was published and republished across several outlets and languages:
