Automated Data Collection from IoT/Field Sensors

Purpose

1.1. Enable seamless, real-time gathering and aggregation of operational, environmental, and equipment data from distributed IoT/field sensors across diverse agricultural assets, optimizing farm workflows, monitoring crop/soil conditions, supporting predictive analytics, and triggering automated management interventions.
1.2. Reduce manual inspection, streamline record-keeping, and improve data-driven decision-making by centralizing sensor data feeds into cloud dashboards, enterprise resource planning (ERP), and analytics platforms.
1.3. Enable remote alerts, condition-based maintenance, compliance documentation, and automated reporting using structured sensor data across all farms and facilities.

Trigger Conditions

2.1. Periodic scheduled data pull (e.g., every 5 minutes) from sensor endpoints.
2.2. Webhook event from a sensor IoT gateway on threshold breach (temperature, humidity, moisture, pH, etc.).
2.3. Device power-up or system restart detected via network ping or MQTT channel.
2.4. Batch upload of local data logs at shift end or WiFi reconnect.
2.5. API call on equipment status change or sensor anomaly.

Platform Variants

3.1. Azure IoT Hub
• Function: Device-to-cloud message ingestion
• Setting: Configure device connection string, define routing to Azure Stream Analytics/API endpoint
3.2. AWS IoT Core
• Feature: Rules Engine — rule actions for invoking AWS Lambda, S3, DynamoDB
• Setting: Topic filter, JSON payload schema, Lambda integration ARN
3.3. Google Cloud IoT Core
• Feature: Device telemetry upload via MQTT/HTTP bridge
• Setting: Registry ID, device credentials, Cloud Pub/Sub topic
3.4. IBM Watson IoT Platform
• Feature: Real-time event ingestion, Cloud Functions trigger
• Setting: API Key setup, device type and event callback URL
3.5. ThingSpeak
• Feature: HTTP REST API for real-time channel data update
• Setting: Channel API key, JSON payload per sensor reading
3.6. Losant
• Feature: Device state report and Workflow trigger
• Setting: Device ID, MQTT broker URI, cloud workflow endpoint
3.7. Particle
• Feature: Webhook forwarding on variable or event update
• Setting: Particle webhook URL, event trigger config
3.8. Blynk IoT
• Feature: HTTP webhook or Blynk.Cloud API stream
• Setting: Auth token, virtual pin assignments, endpoint config
3.9. Ubidots
• Feature: Data ingestion API, dashboard trigger
• Setting: Device label, token, endpoint URL, variable mapping
3.10. Adafruit IO
• Feature: MQTT/REST data feeds, event webhook
• Setting: Feed key, user API key, webhook path
3.11. Airtable
• Feature: REST API for aggregating sensor logs
• Setting: Base ID, API Key, sensor data schema
3.12. InfluxDB Cloud
• Feature: Write API for time-series sensor data
• Setting: Bucket name, organization, token, data precision
3.13. Bosch IoT Suite
• Feature: Device Management REST, event streaming
• Setting: Tenant ID, device registry endpoint
3.14. SAP Leonardo IoT
• Feature: Data ingestion API, rule-based triggers
• Setting: Thing ID, capability assignment, API access
3.15. OpenSensors
• Feature: MQTT broker, API stream connection
• Setting: Topic subscription, API token, endpoint URL
3.16. Temboo
• Feature: Choreos for device data push
• Setting: Account credentials, Choreo input fields
3.17. MyDevices Cayenne
• Feature: MQTT/REST updates, trigger rules
• Setting: Device endpoint, dashboard mapping
3.18. Kaa IoT Platform
• Feature: Data collection REST API
• Setting: Application token, device profile, endpoint list
3.19. TagoIO
• Feature: Device-to-cloud API, real-time trigger
• Setting: Device token, data bucket, trigger rule
3.20. Node-RED
• Feature: MQTT input node, HTTP endpoint
• Setting: Flow credentials, sensor topic, endpoint mapping
3.21. Honeywell Forge
• Feature: Enterprise asset connectivity API
• Setting: Device registration, asset model, data endpoint
3.22. PTC ThingWorx
• Feature: Data ingest APIs, industrial connectors
• Setting: Thing template, Data Shape, REST endpoint

Benefits

4.1. Enables continuous farm monitoring, maximizing crop yield and reducing risk through real-time interventions.
4.2. Centralizes data from heterogeneous sensors for unified operational insight.
4.3. Automates compliance logging, equipment diagnostics, and maintenance scheduling.
4.4. Reduces labor and error from manual readings, supporting data-driven agronomic decisions.
4.5. Scalable setup across multiple farms, devices, and data formats, boosting long-term efficiency and traceability.