FST Graduate Research Topic


Research Topic: Agriculture: Precision Irrigation System
Suggested By: Harris Wang
Last Update: September 28th, 2025

Constrained Object Hierarchies (COH) provides a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains.


Formally, a COH is defined as a 9-tuple:


O = (C, A, M, N, E, I, T, G, D)


Where:



  • C: Components – sub-objects organized hierarchically

  • A: Attributes – state variables describing object properties

  • M: Methods – executable actions associated with components

  • N: Neural Components – adaptive models for learning and inference

  • E: Embedding Neural Component – semantic representation of the COH

  • I: Identity Constraints – rules ensuring object integrity

  • T: Trigger Constraints – event-condition-action rules for dynamic behavior

  • G: Goal Constraints – optimization objectives guiding intelligent behavior

  • D: Constraint Daemons – real-time monitors enforcing system constraints





Based on the COH framework, the modelling process and result of Precision Irrigation System are in Agriculture as follows:


System Description and Significance: Precision agriculture systems optimize resource usage by tailoring irrigation to specific crop needs and environmental conditions. These systems are significant for addressing water scarcity challenges while maintaining agricultural productivity, particularly in regions affected by climate change.


Analysis and Design: The system must monitor soil conditions, predict water requirements, and control irrigation equipment while balancing crop health with resource conservation. Neural components model evapotranspiration and crop response, while identity constraints enforce minimum water requirements for plant survival.


Modelling Solution:



  • C: {FieldZone1, FieldZone2, ... WeatherStation, WaterReservoir}

  • A: soil_moisture, weather_forecast, crop_type, growth_stage

  • M: open_valve(), close_valve(), calculate_water_need()

  • N: EvapotranspirationModel, YieldPredictionModel

  • E: Embedding of field's overall water health and crop status

  • I: Soil moisture must not drop below crop-specific wilting point (plant_health)

  • T: IF forecast indicates_heavy_rain THEN delay_scheduled_irrigation

  • G: Maximize_crop_yield AND Minimize_water_usage

  • D: LeakDetectionDaemon monitoring water flow rates against expected usage


The COH framework enables the irrigation system to make context-aware decisions that balance competing objectives, using predictive models to optimize water usage while maintaining crop health through carefully designed constraints.





You may choose this topic and pursue one of the following routes:


📝 Essay



  • Survey existing methodologies for modeling, designing, and implementing similar intelligent systems.

  • Include:

    • Literature synthesis

    • COH framework analysis

    • Critical evaluation of competing approaches




🛠️ Project



  • Design and implement the system using the COH model.

  • Include:

    • System architecture and design

    • Development and testing

    • Demonstration of core functionality




🎓 Thesis



  • Explore COH’s theoretical foundations in depth.

  • Include:

    • Proposed enhancements or extensions

    • Implementation and evaluation

    • Development of an advanced intelligent system grounded in COH principles