Digital Twins in Agriculture

Organizations appearing across the listed agriculture-twin approaches and applications include IBM, Microsoft, Siemens AG, Deere & Company, AGCO Corporation and Trimble. The evidence spans process simulation, AI models, sensor-connected systems, and crop, irrigation, livestock, greenhouse and machinery use cases. The facts do not establish that each organization offers a dedicated commercial farm-twin product.

Iowa State University, China Agricultural University and Jiangsu University are among the research organizations identified in FACTS. Iowa State researchers studied farmland tile-drainage simulation and AI-enabled soil-structure sensing. China Agricultural University works on farm twins and machinery optimization, while Jiangsu University focuses on machinery damage prediction, maintenance and intelligent-agriculture control.

FACTS records $164.8 million across 94 grants since 2020. It also lists 6 funding rounds totaling $272,998,746, but the round-level entries include projects that are not clearly agriculture-specific. The grant figure is therefore the more direct aggregate funding indicator available for this topic.

LandScan LLC and KinNECTed AI Inc each hold 2 patents in the listed patent-holder data. Other listed holders, including the Electronics and Telecommunications Research Institute, University of Porto and Farmers Business Network, have 1 each. The counts are small, so they identify the leading holders in these facts rather than establishing broad market control.

FACTS lists 18 patents in the 24-month window and patent growth of -50.0%. Publication activity moved in the opposite direction, with 371 publications in the last year and growth of 46.1%. This divergence suggests that recent activity is more visible in research than in patent filings, though the figures alone do not show why.

In September 2026, the University of Jaén and International University of Rioja proposed a modular olive-orchard twin using satellite data and predictive AI. A September 2026 study from the National Centre of Excellence Mechatronics and Clean Technologies and Technical University of Sofia combined IoT soil monitoring with irrigation and energy management. In February 2026, the Australasian Space Innovation Institute launched a national agricultural twin based on geospatial and farm data.

FACTS identifies process-based simulation, AI and data-driven models, IoT-enabled cyber-physical systems, geospatial and Earth observation, and 3D and immersive simulation. Process models represent known mechanisms; AI learns patterns from data. IoT systems keep models connected to physical equipment, geospatial methods provide field-scale context, and 3D tools support visualization. Each approach faces trade-offs in calibration, data quality, integration or detail.

The United States has the largest listed document count, with 4,866, followed by the United Kingdom with 2,270 and Germany with 2,174. France, Japan, China and India also appear among the countries with substantial activity. These counts describe the countries associated with documents in FACTS and are not exclusive shares of global activity.

FACTS lists irrigation and water management (222 documents), greenhouse and controlled-environment farming (227), and field crop management (189). Other applications include livestock and dairy farming, and agricultural machinery and robotics. Recent research also explores animal-health monitoring, pasture management, orchard modeling and autonomous farm operations.