IoT-Enabled Farming Course

Lesson 1 • The Smart Farming Ecosystem

Maps stakeholders, technology vendors, and data flows across the agri-IoT value chain. Connects ecosystem roles to practical deployment decisions.

Lesson 2 • Core IoT Architecture and Components

Explains sensors, actuators, gateways, and cloud layers. Students map each component to a real farm use case.

Lesson 3 • Connectivity Technologies for Farms

Compares wireless protocols suited to rural environments. Students select appropriate connectivity for given farm scenarios.

Lesson 1 • Remote Sensing and Drone Integration

Extends ground-level sensing with aerial imagery and satellite data. Students combine remote and in-field data for comprehensive crop assessment.

Lesson 2 • Sensor Calibration and Maintenance

Teaches calibration procedures and preventive maintenance schedules. Ensures data accuracy over the full growing season.

Lesson 3 • Types of Agricultural Sensors

Catalogues soil, weather, crop, and livestock sensors with their measurement principles. Provides the vocabulary needed for sensor selection decisions.

Lesson 4 • Data Quality and Validation

Introduces outlier detection, missing-value handling, and data validation pipelines. Links data quality directly to decision reliability.

Lesson 1 • Cloud Storage for Agricultural Data

Compares time-series databases, data lakes, and relational stores for farm data. Students choose storage architectures based on query and retention needs.

Lesson 2 • Data Ingestion and Messaging Protocols

Covers MQTT, HTTP, and AMQP for transmitting sensor data to the cloud. Students implement a basic ingestion pipeline for farm telemetry.

Lesson 3 • Data Integration and Interoperability

Addresses API design, data standards, and integration with farm management software. Students connect heterogeneous data sources into a unified view.

Lesson 4 • Real-Time Data Streaming and Processing

Introduces stream processing for immediate alerts and control triggers. Connects real-time processing to automated farm responses.

Lesson 1 • Principles of Precision Agriculture

Defines precision agriculture, its economic rationale, and its dependence on IoT data. Frames the chapter's applied techniques within a strategic context.

Lesson 2 • Variable Rate Application Systems

Covers variable-rate irrigation, fertilisation, and pesticide application hardware. Students configure a variable-rate controller using prescription map data.

Lesson 3 • Soil Mapping and Zone Management

Teaches soil sampling, interpolation, and management zone delineation. Students produce a soil variability map from sensor and lab data.

Lesson 1 • IoT Sensors for Irrigation Management

Covers tensiometers, capacitance probes, and weather-based sensors used in irrigation control. Students select and deploy sensors for a drip or sprinkler system.

Lesson 2 • Automated Irrigation Control Systems

Teaches controller programming, threshold-based triggers, and feedback loops. Students build an automated irrigation schedule using sensor inputs.

Lesson 3 • Water Conservation and Efficiency Metrics

Introduces irrigation efficiency indices and water audit procedures. Students calculate water savings achieved through IoT-driven scheduling.

Lesson 1 • Animal Health and Behaviour Monitoring

Covers activity, rumination, and temperature monitoring for early disease detection. Students configure alert thresholds for common health events.

Lesson 2 • Herd Performance Analytics

Applies aggregated sensor data to herd-level KPIs and benchmarking. Students generate a herd performance report from collected IoT data.

Lesson 3 • Automated Feeding and Milking Systems

Explains IoT integration in precision feeding and robotic milking. Students map data flows between animal ID systems and automated equipment.

Lesson 1 • Predictive Modelling for Crop and Livestock

Introduces regression, classification, and time-series forecasting applied to farm outcomes. Students train and evaluate a yield or health prediction model.

Lesson 2 • Exploratory Data Analysis for Farm Data

Applies descriptive statistics and visualisation to identify patterns in sensor datasets. Prepares students to ask the right analytical questions before modelling.

Lesson 3 • Reporting and Stakeholder Communication

Teaches data storytelling and report design for farm owners, investors, and regulators. Students produce a concise performance report from analytics outputs.

Lesson 4 • Machine Learning in Precision Agriculture

Covers supervised and unsupervised ML techniques for crop stress and anomaly detection. Students apply a clustering algorithm to segment field management zones.

Lesson 1 • ROI Measurement and Business Case Development

Provides frameworks for quantifying IoT benefits and building investment proposals. Students calculate ROI for a defined IoT use case using real cost and yield data.

Lesson 2 • Cybersecurity for Agricultural IoT

Identifies attack surfaces, authentication methods, and encryption standards for farm IoT networks. Students conduct a basic security audit of a sample farm network.

Lesson 3 • Firmware Updates and Device Lifecycle

Covers over-the-air update strategies and end-of-life device management. Ensures students can maintain a secure and current device fleet at scale.

Lesson 4 • Scaling IoT from Pilot to Enterprise

Addresses infrastructure scaling, multi-site management, and vendor lock-in mitigation. Students design a phased rollout plan for a multi-farm operation.