Agentic Intelligence
An AI workflow that interprets a question, selects relevant tools and data, and assembles contextual outputs within defined permissions.
Understand the concept01 / THE PHYSICAL WORLD, EXPLAINED
From Food-Ag to forests, water, energy and infrastructure. Understand the concepts behind physical-world intelligence, without needing to be a GIS, climate or domain expert.
02 / EXPLORE THE GLOSSARY
19 concepts · AI + Geospatial
Clear filters ↗An AI workflow that interprets a question, selects relevant tools and data, and assembles contextual outputs within defined permissions.
Understand the conceptLearn what agricultural data spaces are, their key applications, benefits, and role in secure data sharing, interoperability, AI, and traceability.
Understand the conceptLearn how agricultural knowledge graphs connect crop, soil, weather and farm data to support search, reasoning and AI-driven decisions.
Understand the conceptAI in agriculture uses machine learning, satellite data, and predictive models to improve yields, reduce risk, and enable smarter farming decisions. Learn the definition and real-world applications.
Understand the conceptAI-powered yield prediction uses data models, weather, and field insights to estimate crop output and support planning decisions.
Understand the conceptComparing observations from different times to identify changes in an area, feature or measured condition.
Understand the conceptCrop intelligence uses data, AI, and analytics to monitor crop health, predict risks, and support better farm and agribusiness decisions.
Understand the conceptThe recorded origin and processing history of data, including its source, observation time and transformations.
Understand the conceptLearn what decision-grade intelligence in agriculture means, its key applications, benefits, and role in smarter farming and agribusiness decisions.
Understand the conceptCollecting information about Earth’s land, water and atmosphere using satellites and other observation systems.
Understand the conceptA farm AI copilot uses agricultural data and AI to provide real-time guidance for crop planning, irrigation, risk management, yields, and farm operations.
Understand the conceptFarm data interoperability connects agricultural systems, enabling seamless data exchange, connected workflows, better collaboration, and smarter farm decisions
Understand the conceptGeospatial intelligence in agriculture uses satellite imagery and location data to monitor crops, map field boundaries, and detect land use change. Learn the definition and key applications.
Understand the conceptInsight derived by relating location-based observations to the assets, environments and activities they describe.
Understand the conceptLand cover describes the physical surface, such as forest, water or built-up land; land use describes how people use that area.
Understand the conceptThe limits to confidence in a model result arising from incomplete observations, assumptions and imperfect representations of reality.
Understand the conceptDecision context built by connecting observations of the physical world with domain knowledge, models and operational questions.
Understand the conceptRemote sensing uses satellite and drone data to monitor crops, soil, and weather conditions for better farm management and planning.
Understand the conceptSatellite imagery helps monitor crop health, detect stress, and assess field conditions using remote sensing data.
Understand the concept03 / FROM CONCEPT TO CONTEXT
The glossary explains the language. Cropin connects workflows, data and AI to your operating context. Food-Ag, forest and water intelligence are available; energy and infrastructure workflows are upcoming and can be explored with partners.
Explore the platform ↗Partner on a use case ↗