Case Study: State Machine Design for a Smart Garden Irrigation Controller

1. Introduction

Modern gardening and agriculture increasingly rely on automation to optimize resource use, particularly water — a scarce resource in many regions. A smart irrigation controller automates watering based on real-time soil conditions rather than fixed timers, reducing waste, preventing over- or under-watering, and supporting healthier plant growth.

This case study focuses on the behavioral modeling of such a system using a UML state machine diagram (also called a statechart diagram). The diagram captures the system’s lifecycle, decision points, and responses to events such as moisture readings, timeouts, and user intervention.

The design uses PlantUML syntax, similar to the coffee shop example provided, which elegantly models composite states, guards, actions, and error/recovery paths.

2. Problem Statement & Requirements

An automated irrigation controller for a home garden or small greenhouse must:

• Start in a low-power Standby mode most of the time.
• Periodically wake up according to a schedule (timer trigger) to check conditions.
• Enter a Sensing state to read soil moisture level (via capacitive or resistive sensor).
• If moisture < 30% (configurable dry threshold), start Irrigating by opening a solenoid valve or activating a pump.
• If moisture ≥ 30%, return to Standby (no watering needed).
• While Irrigating, continuously (or periodically) monitor moisture.
• Stop irrigating and close the valve when:
  • Moisture reaches 80% (configurable wet threshold) → target achieved.
  • A Safety Timeout expires (e.g., 30 minutes) → prevents flooding, pipe bursts, or electrical issues if sensor fails.
• After stopping irrigation, move to Shutdown state.
• In Shutdown, wait for manual confirmation (button press or app command) before returning to Standby — this allows the user to inspect the system or override if needed.
• Handle faults gracefully (e.g., sensor failure, valve stuck) by transitioning to an Error state with recovery options.

Additional desirable behaviors (kept simple here):

• No irrigation during certain hours (handled by schedule/timer).
• Logging or notifications are out of scope for this core state machine.

3. Key State Machine Concepts Used

• States: Idle/Standby, Sensing, Irrigating, Shutdown, Error.
• Composite state: Irrigating includes internal monitoring logic (though kept flat here for simplicity).
• Transitions:
  • Triggered by events (timer, moisture reading, timeout).
  • Guarded by conditions [moisture < 30%], [moisture >= 80%].
• Actions: /open_valve(), /close_valve(), /notify_user(), etc.
• Initial / final pseudostates: [*] for start/end.
• Self-transitions and recovery loops.

4. State Diagram in PlantUML

Below is the complete PlantUML code implementing the described behavior. It follows conventions from the coffee shop example (skinparam styling, composite states where appropriate, guards in [], actions with /).

Explanation of the Diagram

• Standby — Default low-power/idle state.
• Sensing — Quick check triggered by timer; avoids unnecessary watering.
• Irrigating (composite) — Active watering phase with internal Watering sub-activity.
  • Exits on target moisture or safety timeout.
• Shutdown — Post-irrigation hold state requiring confirmation to resume automation (safety feature).
• Error — Fault containment state with manual recovery transition.

5. Design Rationale & Benefits

• Water conservation — Only irrigates when actually needed (soil moisture-based instead of time-based).
• Flood prevention — Dual exit conditions from Irrigating (moisture target + timeout).
• User safety & control — Manual confirmation after abnormal stop prevents automatic restart after potential issues.
• Extensibility — Easy to add states (e.g., Rain Detected, Low Battery, Winter Mode) or adjust thresholds.
• Low complexity — Flat where possible, composite only where logical grouping adds clarity (Irrigating).

This design balances robustness, safety, and simplicity — suitable for embedded microcontroller implementation (Arduino, ESP32, etc.).

6. Conclusion

State machines provide an excellent formalism for modeling reactive control systems like smart irrigation controllers. By clearly defining states, events, guards, and actions, engineers can reason about system behavior, edge cases, and error recovery before writing code.

The PlantUML representation above serves as both documentation and a blueprint for implementation. Rendering it (via PlantUML tools or online servers) produces a clean, professional diagram ready for requirements reviews, code generation, or teaching UML concepts.

Future extensions could include:

• Weather API integration (skip Sensing if rain forecast).
• Multiple zones with per-zone moisture thresholds.
• Mobile app notifications on timeout or error.

This case study demonstrates how a seemingly simple automation problem benefits greatly from structured state-based modeling.

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