Agriculture is the word’s largest user of water, accounting for 70% of total freshwater consumption. This puts farms in direct competition with humans and animals for Earth’s most precious resource. While some farmers struggle to get enough water due to drought, others waste up to 50% of the water used in crop irrigation. Precision irrigation based on environmental conditions can reduce water consumption by as much as 50% in some cases. Predicting when crops need irrigation is even better.
IoT Blueprint:
Assemble the collection of components displayed below to address the use case of reducing water usage on farms.
Sensor(s)
Temperature: Detect Levels of Heat that Increase the Rate of Water Evaporation
Humidity: Detect Amount of Water in the Air Around Crops
Moisture: Detect Soil Moisture Levels for a Given Crop and Soil Type
Weather API: Anticipate Periods of Rain and Dryness
LoRa: Create Low Bandwidth Coverage Needed to Reach Internet Access
Cellular: If Coverage Available and Cost-Effective for High Bandwidth Scenarios
Satellite: When No Terrestrial Coverage Options are Available for Medium Bandwidth Scenarios
Digital Twin Modeling
Individual and Collections of Crops in a Given Block
Data Processing + Storage Location(s)
Edge: On the Farm
Cloud: Filtered Data Relayed from Edge to Monitor Broader Areas of one or More Pastures or Farms
Streaming Analytics
Compare Sensor Data Values to Defined Setpoints, KPI Value Ranges and Thresholds
Filter out Duplicate Sensor Data Value Readings
Automation
Green KPI: No Action
Yellow KPI: Warn Notification for Awareness When Sensor Values are Trending Towards Dry Conditions
Red KPI: Alert Notification to Farm Workers in the Field of Day Conditions to Manually Turn Open or Close Irrigation System Valves or to Automate the Activation and Deactivation of Irrigation Systems
People
Deploy and Maintain Solution
SMEs Define KPIs and Actions
Farmers, Agronomists, and Farm Workers in the Fields
Security
Uniquely Identify Each Device
TLS 1.3 Encryption for Data in Transit
Encrypt Data at Rest
Validate Device Messages to Ensure they use Expected Data Format
Rotate Security Tokens
Limit IP Address Ranges
AI Anomaly Detection
Zero Trust: Reauthenticate Device Messages Through Every Step of the System
Artificial Intelligence
Machine Learning Time Series Forecasting Model to Predict Future Occurrence of Dry Conditions
IoT Platform
Device SDK Captures Sensor Data from Physical Twin and Securely Sends it as a JSON Payload to the IoT Platform Along with a Unique Identifier and Security Token
IoT Platform Captures, Authenticates, and Saves Incoming Device + Sensor Data to a Message Queue
Background Process Takes Queued Data and Hydrates the Digital Twin by Saving it to a Database Table that Mimics the Structure of the Physical Twin
AI Model is Trained and Retrained with Digital Twin Dataset of Current and Historical Data Where Properties of Twin are Mapped to ML Features
Hot Path Data is Sent to Streaming Analytics to Facilitate Real Time Alerting and Automation
Same Hot Path Data is Also Sent to AI Model for Inferencing to Predict Future Occurrences
Rob is a writer, teacher, speaker, world traveller and undersea explorer. He's also a thought leader in the areas of enterprise mobility and the Internet of Things.
