Data collection from IoT farm sensors

Purpose

1.1. Automate remote data collection, aggregation, and reporting from distributed IoT farm sensors for real-time education, farm operations monitoring, and decision support in agriculture training.
1.2. Automate protected storage, analytics, and trigger-based dissemination of sensor insights to students, instructors, and operations teams.
1.3. Automate alerting, predictive analytics, and workflow kickstarts for both demonstrations and live operational scenarios.

Trigger Conditions

2.1. Sensor data threshold crossed (e.g., soil moisture < set point).
2.2. Scheduled polling interval (e.g., every 10 minutes).
2.3. Manual or API-initiated automator event from instructional staff.
2.4. Sensor network connectivity change (online/offline automated triggers).
2.5. External environment event (e.g., weather API predicts rain).

Platform variants

3.1. AWS IoT Core
- Feature/Setting: Rule Engine — configure rule to forward MQTT sensor messages to Lambda for automated data processing.
3.2. Microsoft Azure IoT Hub
- Feature/Setting: Automate message routing — set up routes to Azure Functions for automating data analytics workflows.
3.3. Google Cloud IoT Core
- Feature/Setting: Device registry — automate device registration, set up Pub/Sub push for automated data pipelines.
3.4. Losant
- Feature/Setting: Workflow Builder — drag-and-drop automation for ingestion and rule-based processing of farm sensor data.
3.5. ThingsBoard
- Feature/Setting: Rule Chains — automate telemetry event detection and notification flows.
3.6. Ubidots
- Feature/Setting: Events — automate trigger/actions when a farm sensor value exceeds preset thresholds.
3.7. Particle Cloud
- Feature/Setting: Webhooks — automatedly forward sensor data to external web services for downstream automation.
3.8. Blynk
- Feature/Setting: Automation Events — configure project to automate farm training based on sensor input.
3.9. TagoIO
- Feature/Setting: Analysis Scripts — automate execution of data handling scripts at each incoming message.
3.10. Node-RED
- Feature/Setting: Flows — automate sensor data transformation, storage, and alerting visually.
3.11. Home Assistant
- Feature/Setting: Automations — automate agricultural device responses to sensor data.
3.12. Zapier
- Feature/Setting: Webhooks & Code steps — automate integration between IoT sensors and educational workflows.
3.13. Make (Integromat)
- Feature/Setting: Scenario — automate multi-step processes triggered by new IoT readings.
3.14. Balena
- Feature/Setting: Supervisor API — automate device updates and data logging.
3.15. Cayenne
- Feature/Setting: Triggers — automate alerts or dashboard updates from IoT sensor shifts.
3.16. IBM Watson IoT Platform
- Feature/Setting: Rules & Actions — automate messaging, cloud function triggers, and device management.
3.17. ThingSpeak
- Feature/Setting: React App — configure automated responses to data patterns.
3.18. OpenRemote
- Feature/Setting: Rules Engine — automate decision-making based on farm sensor input.
3.19. Adafruit IO
- Feature/Setting: Triggers — automate output pin actions or email notification when new sensor data received.
3.20. PTC ThingWorx
- Feature/Setting: Event/Subscription — automate logic execution and data forwarding in educational IoT deployments.

Benefits

4.1. Automates collection and organization of critical farm data, reducing manual record-keeping.
4.2. Enables real-time automated insights and training interventions by instructors.
4.3. Automates the creation of assessment and demonstration scenarios for farm school participants.
4.4. Improves precision farming education by automating feedback on conditions and student actions.
4.5. Reduces resource waste and increases safety by automating risk alerts and device management.
4.6. Facilitates automated compliance, reporting, and research data builds for education and farm management.