Universal connectivity
Connect field sensors, weather stations, drones, machinery, robots, cloud platforms, models, and third-party services through common interfaces.
An Adaptive AgroTech innovation
AGRIOS Agricultural Intelligence Operating System
A universal framework for connecting sensors, data platforms, simulation models, digital twins, artificial intelligence, machinery, and autonomous robots within one interoperable agricultural ecosystem.
Vendor-independent architecture
Secure by design
Modular and extensible
IoT Sensors
Digital Twins
AI & Analytics
Robots & Machines
Farm Systems
Cloud & APIs
Why AGRIOS
One ecosystem. Every agricultural technology.
Digital agriculture is fragmented across proprietary devices, isolated software, incompatible data formats, and disconnected machines. AGRIOS provides the common architecture that allows these technologies to work together.
Open architecture
A modular, vendor-independent framework designed to support existing technologies while remaining adaptable to future protocols, devices, and services.
Modular intelligence
Start with sensing and monitoring, then add analytics, digital twins, AI advisory, mission planning, and autonomous execution as requirements evolve.
Built for agriculture
Designed around agricultural entities, operations, environmental conditions, biological processes, spatial data, and real-world farm workflows.
The AGRIOS logic
Infrastructure. Intelligence. Action.
AGRIOS organizes digital agriculture around a continuous perception–reasoning–action loop.
Infrastructure
Observe physical agricultural systems and transport reliable, contextualized data from the field to edge or cloud environments.
Intelligence
Transform data into knowledge, predictions, recommendations, optimized plans, and machine-readable decisions.
Action
Execute approved decisions through agricultural machinery, robots, actuators, control systems, and coordinated autonomous fleets.
Reference architecture
Seven layers from raw data to autonomous operation.
The layers form a maturity pathway, but they are not isolated silos. AGRIOS enables information, commands, feedback, and services to move securely across the complete ecosystem.
Select any layer to open its technical explanation and exampleRole in AGRIOS
This layer provides a standard way for physical and virtual data producers to identify themselves, describe their measurements, and publish observations.
Standardized information
- Device identity and manufacturer
- Sensor type, unit and accuracy
- Location, timestamp and sampling rate
- Calibration and quality flags
Practical example
A soil-moisture node publishes 21.8% VWC every 10 minutes through LoRaWAN. AGRIOS maps the vendor payload into a common Observation message.
agrios/device/register → capability: soil_moisture
agrios/observation/publish → value: 21.8, unit: percent
Role in AGRIOS
Raw messages are converted into trusted agricultural records linked to fields, crops, devices, operations, and users.
Standardized information
- Schema validation and unit conversion
- Time-series and spatial storage
- Metadata and provenance
- Access control, backup and APIs
Practical example
The platform converts Fahrenheit to Celsius, rejects impossible readings, attaches the observation to Greenhouse 4, and stores both raw and validated values.
raw_payload → validation → normalization → context → storage → API
Role in AGRIOS
Alerts combine thresholds, persistence, operating context, crop stage, weather, and equipment state so users are notified only when action is meaningful.
Standardized information
- Threshold and duration rules
- Missing-data and battery alarms
- Context-aware crop warnings
- Escalation and acknowledgement
Practical example
Humidity above 90% for 45 minutes during nighttime tomato production triggers a disease-risk warning, but a short irrigation-related spike is ignored.
WHEN RH > 90% FOR 45 min AND crop=tomato AND phase=night → alert
Role in AGRIOS
Analytics transforms continuous records into interpretable indicators for farmers, researchers, managers, and machines.
Standardized information
- Trend and anomaly analysis
- Operational efficiency metrics
- Energy and water KPIs
- Yield and maintenance forecasts
Practical example
AGRIOS calculates the percentage of each day that VPD remained inside the crop target and compares energy consumption against similar production days.
KPI: climate_compliance = minutes_in_target / total_minutes
Role in AGRIOS
A twin is registered as a service with defined inputs, states, parameters, outputs, validity limits, and update frequency.
Standardized information
- Twin identity and represented asset
- Input/output interface
- State synchronization
- Scenario and what-if execution
Practical example
A greenhouse twin receives outdoor weather and indoor climate, simulates alternative ventilation settings, and returns expected temperature, RH and energy use.
POST /twins/greenhouse-04/simulate { ventilation: 65, horizon: "6h" }
Role in AGRIOS
AI services consume validated data and simulation results, then produce recommendations with evidence, confidence, constraints, and approval requirements.
Standardized information
- Explainable recommendations
- Diagnosis and optimization
- Natural-language data access
- Human approval and audit trail
Practical example
The advisor recommends opening roof vents to 55%, explains the humidity and disease-risk evidence, and shows the expected twin-predicted outcome.
recommendation: ventilation=55% | confidence=.88 | requires_approval=true
Role in AGRIOS
Robots and machines expose capabilities rather than proprietary controls. AGRIOS assigns compatible missions, monitors execution, and records results.
Standardized information
- Robot capability registration
- Mission description and assignment
- Safety state and supervision
- Execution feedback and reporting
Practical example
A scouting robot advertises navigation, RGB imaging and sampling capabilities. AGRIOS sends a crop-row mission only after safety and battery checks pass.
mission/create → capability: scout_crop_rows → robot selects/accepts → live status
AGRIOS Core
The common operating layer between agriculture and technology.
AGRIOS Core is not simply a cloud dashboard. It is the interoperability layer that registers devices, understands agricultural entities, transports messages, manages services, coordinates missions, and controls access across edge, cloud, and robotic systems.
✓
Common data and capability modelEvery device, model, service, and machine describes what it is and what it can do.
✓
Event-driven communicationSystems can publish data, subscribe to events, request services, and receive commands.
✓
Edge–cloud–robot continuityWorkloads can run locally, remotely, or across hybrid infrastructure without changing the agricultural logic.
Select an AGRIOS Core module
Click any block above to see its purpose, interfaces and a practical example.
Interoperability first
Designed to bridge existing technologies.
AGRIOS does not replace established industrial, IoT, robotics, or web standards. It provides agricultural semantics and coordination above them.
MQTT
LoRaWAN
ROS 2
OPC UA
CAN / CANopen
Modbus
REST APIs
GraphQL
WebSockets
Wi-Fi
Bluetooth LE
Cellular / NB-IoT
GNSS
Edge AI
Docker
Cloud Services
Application domains
A common foundation for every agricultural environment.
Greenhouses
Climate sensing, crop models, energy optimization, digital twins, control, and mobile robotic operations.
Field Crops
Spatial sensing, crop monitoring, decision support, machinery coordination, and autonomous field missions.
Livestock
Environmental monitoring, animal data, health analytics, facility twins, alerts, and automated intervention.
Orchards & Vineyards
Microclimate networks, remote sensing, disease risk models, targeted spraying, and robotic scouting.
Agricultural Machinery
Telemetry, implement data, operational analytics, task exchange, fleet coordination, and predictive maintenance.
Agricultural Robotics
Robot registration, capability discovery, mission planning, supervision, interoperability, and shared autonomy.
Remote Sensing
Satellite, UAV, and proximal imagery integrated with field observations, models, and machine operations.
Research & Innovation
A shared experimental architecture for universities, research centers, living labs, and technology developers.
End-to-end implementation examples
How a new component becomes part of AGRIOS.
These use cases show that AGRIOS is not one proprietary device or dashboard. It is the shared contract that lets independently developed technologies discover one another, exchange meaning, and cooperate safely.
Adding a soil-moisture IoT node
A third-party manufacturer can connect a LoRaWAN, cellular, Wi-Fi or wired sensor without rewriting the complete platform. The device adapter maps its native payload into the AGRIOS device and observation models.
1
Acquire
The node measures soil moisture, temperature, battery and signal quality.
2
Manage
Payload is decoded, units normalized, timestamps checked and data linked to Field A.
3
Alert
AGRIOS detects low battery, missing packets or moisture below the crop threshold.
4
Analyze
Daily depletion rate and spatial irrigation uniformity are calculated.
5
Simulate
A soil-water twin predicts moisture under alternative irrigation amounts.
6
Advise
AI recommends when and how much to irrigate, with evidence and confidence.
7
Act
After approval, the irrigation controller receives a bounded command.
What the vendor supplies
Device manifest, payload decoder, measurement definitions, units, accuracy, calibration, security identity and communication adapter.
What AGRIOS supplies
Registration, normalized data model, storage, alerts, APIs, analytics access, twin inputs, permissions and audit records.
Result
The same sensor can be used by multiple dashboards, analytics plugins, twins and automation services without vendor-specific integrations each time.
Adding an autonomous field robot
The robot remains controlled by its own safe onboard software, such as ROS 2 or a proprietary controller. AGRIOS standardizes capability discovery, mission exchange, operational state and reporting above that controller.
1
Acquire
Robot publishes pose, battery, safety state, camera observations and implement telemetry.
2
Manage
State and observations are linked to robot identity, field, mission and timestamps.
3
Alert
Geofence violation, obstacle, low battery or communication loss creates an event.
4
Analyze
Coverage, productive time, path efficiency and task quality are measured.
5
Simulate
The farm twin checks route feasibility, terrain, energy and expected duration.
6
Advise
AGRIOS recommends the robot, route, speed and operating window.
7
Act
A mission is offered; the robot accepts, executes locally and reports progress.
Robot capability manifest
Navigation modes, implements, sensors, payload, speed, autonomy, supported missions, safety interfaces and required operator permissions.
Mission contract
Objective, geometry, constraints, acceptable conditions, completion criteria, abort logic and reporting requirements.
Critical boundary
AGRIOS coordinates and supervises; safety-critical motion control remains onboard. This separation must be explicit in the architecture.
Adding a greenhouse digital twin
A research team can contribute a mathematical or machine-learning model as a versioned AGRIOS service. The twin declares what it represents, which inputs it needs, what outputs it produces and where its predictions are valid.
1
Acquire
Indoor climate, outdoor weather, crop state and actuator positions feed the twin.
2
Manage
Inputs are synchronized, quality checked and transformed into twin variables.
3
Alert
AGRIOS flags missing inputs, model drift or operation outside validity limits.
4
Analyze
Measured and predicted states are compared and model errors tracked.
5
Simulate
The twin runs what-if scenarios for heating, ventilation, shading and cooling.
6
Advise
An optimizer selects a strategy balancing climate, energy, cost and crop risk.
7
Act
Approved setpoints are sent to automation; outcome updates the twin.
Twin manifest
Represented asset, equations or model package, input/output schema, parameters, uncertainty, version, owner and computational requirements.
Operational contract
Update interval, forecast horizon, valid ranges, fallback behavior, calibration method and monitoring metrics.
Reusable value
Any compatible sensor source can feed the twin, and any advisory or control service can consume its standardized outputs.
Complete case: humidity-risk management in a tomato greenhouse
This scenario shows all seven layers working as one closed loop while preserving human oversight and machine safety.
1
Data acquisition
Wireless nodes report RH, temperature, leaf wetness, CO₂ and equipment state.
2
Data management
AGRIOS validates readings, identifies greenhouse zones and aligns external weather.
3
Smart alert
High RH persists at night while leaf wetness rises; disease-risk event is created.
4
Analytics
The system compares the event with historical climate, energy and crop records.
5
Digital twin
Ventilation, heating and dehumidification scenarios are simulated for six hours.
6
AI advisory
AGRIOS explains the best strategy, expected energy cost and risk reduction.
7
Autonomous action
Operator approves; climate controller executes setpoints and reports outcome.
Human role
Policy defines whether recommendations are advisory, approval-required or automatically executable inside safe operating limits.
Feedback loop
New measurements verify whether humidity and disease risk decreased. The twin and analytics update their performance records.
Business value
Reduced crop risk, documented decisions, lower energy waste, reusable integrations and a foundation for subscription services.
Platform roadmap
Build once.
Connect everywhere.
AGRIOS is being developed as the commercial agricultural intelligence ecosystem of Adaptive AgroTech, with an open and extensible architecture for research partners, developers, equipment manufacturers, and technology providers.
Select a roadmap component
Click any block to understand the concrete artifact that must be designed and delivered.
Help shape the operating framework for digital agriculture.
Adaptive AgroTech welcomes collaboration with universities, research centers, technology companies, equipment manufacturers, software developers, and agricultural organizations.