Project-based Learning Information: Functional Programming 2026
Smart Soil IoT
Soil Monitoring System for Peatland Agriculture with Intelligent Decision Support
Lecturer’s Main Task: Ensure that the project and its outcomes are verifiable, ensure that functional thinking has been properly implemented throughout the development process, identify opportunities for scientific publication when sufficient novelty is present, and provide opportunities for students who make substantial inventive contributions to be involved in potential patent applications
Module 1: Soil Sensor Acquisition and Validation
Concern: How can we acquire reliable soil data in peatland conditions? and implment the IoT?
Main tasks:
- Select the required soil sensors.
- Design the IoT sensor node.
- Waterproofing and protection from high moisture.
- Anti-theft enclosure / installation mechanism.
- Sensor calibration.
- Automatic or assisted recalibration.
- Noise filtering and outlier detection.
- Produce validated soil data.
Recommended minimum parameters:
- Soil Moisture
- Soil Temperature
- Soil pH
- Electrical Conductivity (EC)
Module 2 — IoT Communication, Edge Processing, and User Management
Concern: How can validated sensor data be transmitted reliably and securely from the field? Responsibilities: Validated Sensor Data ↓ Edge Processing ↓ Local Buffer ↓ Wi-Fi / LoRa / MQTT ↓ Server Possible features: ESP32-based edge node MQTT or HTTP offline buffering automatic reconnection device identification device-user ownership basic authentication role management sampling interval control edge anomaly checking
Module 3 — Soil Data Platform and Visualization
Concern: How can soil conditions be monitored and understood over time? Main features: IoT Data ↓ REST API ↓ Database ↓ Time-Series Processing ↓ Dashboard Dashboard: current moisture soil temperature pH EC historical graphs device status sensor status location warning history comparison between plots
Module 4 — Soil Analytics and Decision Support
NO DEEP LEARNING for this!
Concern: What does the sensor data actually mean? Pipeline: Historical + Current Data ↓ Feature Processing ↓ Soil Analytics ↓ AI / Rules ↓ Risk / Prediction / Recommendation Possible tasks: anomaly detection soil-condition classification waterlogging risk excessive dryness risk pH change detection EC anomaly trend prediction irrigation recommendation confidence/uncertainty estimation
Module 5 — Plant-Specific Expert System
Concern: How can the system translate general soil conditions into plant-specific assessments and recommendations?
Main responsibilities: Define plant-specific soil requirements. Represent expert knowledge as rules. Support different thresholds for different plants. Support different rules for different growth stages. Combine soil moisture, soil temperature, pH, and EC into plant-specific assessments. Generate warnings and recommendations based on expert rules. Record the source or provenance of each rule. Allow expert validation and rule revision. Provide an inference engine that can execute the rules consistently. Produce explainable outputs showing why a recommendation was generated.
Module 6 — Plugin Framework and Extensibility
Concern: How can the soil monitoring platform be extended with new plants, analytics methods, expert rules, or models without modifying the core system? Main responsibilities: Design a common plugin interface. Support plugin registration and discovery. Allow plugins to declare required soil parameters. Support plant-specific expert rules. Support analytics or AI model plugins. Allow new plugins to be added without changing the core application. Provide plugin metadata such as name, version, author, and requirements. Add enable/disable controls for plugins. Validate plugin compatibility with the platform. Provide at least several demonstration plugins.