🌱 GardenHUB – Autonomous IoT Watering System

A real-world IoT automation system designed to manage irrigation for a ~70 m² home garden using sensor-driven logic, weather integration, and a Raspberry Pi backend.

This project combines:

• Software engineering
• Electronics & wiring
• IoT communication
• Data logging & future ML experimentation
• Automation logic design

It is both a functional irrigation system and an evolving engineering project.

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📸 System Preview

Control Box (Arduino Nano ESP32 Node)

Soil Moisture Sensor (Test Setup)

Raspberry Pi Controller

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Context

Location: Designed for a ~70 m² residential garden in Central Europe.

Infrastructure:

7 raised beds

Greenhouse

Fruit trees

Mediterranean herb patch

Pots & strawberry section

The system supports seasonal vegetable production (salads, tomatoes, onions, garlic, broccoli, potatoes, etc.) with controlled and automated irrigation.

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Project Goals

Phase 1 – Functional Automation (Current)

Sensor-based moisture monitoring

Multi-zone watering control

Web interface for monitoring & manual control

Weather integration

Historical logging in SQLite

Reliable data ingest from ESP32 nodes

Phase 2 – Robust IoT Architecture

Improve communication reliability

Health monitoring of nodes

Better scheduling & fault tolerance

Expand to 6 watering zones

Phase 3 – ML-Assisted Irrigation

Use historical moisture, weather, and watering events

Optimize watering duration

Improve water efficiency

Extend architecture to controlled environments (e.g., mushroom chambers)

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🚧 Current Status

The system is currently in an active development and stabilization phase.

Working components:

• Sensor → Raspberry Pi data pipeline
• SQLite data storage
• Watering decision engine (dry-run mode)
• Web UI for monitoring and manual triggering
• Weather data integration

Ongoing work:

• Backend refactoring (modular architecture)
• Improved reliability and error handling
• Preparation for real valve control (currently disabled)

The system has completed a multi-week real-world test cycle in a home garden environment.

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System Architecture

Central Controller

Raspberry Pi 4B

Raspberry Pi OS (64-bit)

Python 3

Flask backend

SQLite database

Sensor Nodes

Arduino Nano ESP32

Wi-Fi communication (HTTP POST → Flask)

DFRobot Waterproof Soil Moisture Sensor v2.0 (capacitive)

Irrigation Control

24V AC solenoid valves (Hunter / RainBird – TBD)

Relay module control

Planned expansion: up to 6 zones

Current test stage: 2–3 zones, 4–6 sensors

Power System

Mains → 24V AC for valves

Mains → 5V DC for Raspberry Pi & ESP32

Relay isolation for valve actuation

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Design Principles

• Reliability over complexity
• Fail-safe behavior (no watering on missing data)
• Incremental automation (manual → assisted → autonomous)
• Real-world testing before full deployment

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Software Stack

Backend: Python + Flask

Database: SQLite

Communication: HTTP POST (future: MQTT)

Weather API: Open-Meteo

Scheduler: standalone Python process (morning execution window)

Plant configuration: JSON-based profiles

Automation engine: custom watering logic module

Planned: ML pipeline for predictive irrigation

Data & Observability

GardenHUB logs time-series events in SQLite to support traceability and future analytics:

• sensor_readings (timestamp, node_id, zone, moisture, temp/humidity optional)
• watering_events (timestamp, zone, duration, reason/manual/auto)
• weather_snapshots (timestamp, forecast/rain probability/temp)
• system_health (node last_seen, error counts — planned)

This data model supports:

• historical trend analysis
• watering effectiveness evaluation
• future ML features (predictive duration / anomaly detection)

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Repository Structure (pre-refactor)

.
├── app.py                  # Flask entrypoint
├── db.py                   # SQLite connection handler
├── db_schema.py            # Database schema definitions
├── db_init.py              # Table initialization
├── repositories.py         # Data access layer
├── watering_engine.py      # Core watering decision engine
├── watering_decision.py    # Threshold & decision logic
├── garden_logic.py         # Moisture interpretation logic
├── get_weather_new.py      # Weather ingestion
├── historic_weather.py     # Weather history queries
├── python_receiver.py      # Sensor ingest endpoint
├── plants/                 # Plant configuration (JSON)
├── templates/              # Flask templates
├── static/                 # CSS
├── dev_tests/              # Experimental scripts (no secrets)
└── arduino_secrets.example.h

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Security & Configuration

Secrets are not stored in the repository.

Arduino credentials go in:

arduino_secrets.h

(ignored via .gitignore)

Template provided:

arduino_secrets.example.h

Python API keys should be stored in environment variables (.env not committed).

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Quick Start (Raspberry Pi)

1️⃣ Install system dependencies

sudo apt update
sudo apt install -y git python3-venv python3-pip sqlite3

2️⃣ Clone the repository

git clone https://github.com/Katolux/Watering-System.git
cd Watering-System

3️⃣ Create virtual environment

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

4️⃣ Run the application

python3 app.py

Access from another device on the same network:

http://<RASPBERRY_PI_IP>:5000

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Project Scope

This is a physical irrigation system deployed in a real garden environment.

The system includes:

Live sensor ingestion

Backend decision logic

Historical data storage

Weather-based logic

Expandable hardware architecture

The project focuses on backend systems, automation logic, and applied IoT engineering.

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Roadmap

• Basic sensor ingest

• Database logging

• Web UI for monitoring

• Manual watering trigger

• Hardware valve control integration

• MQTT-based communication

• Node health monitoring

• Predictive ML irrigation model

• Mushroom growth chamber integration

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Author

Alfonso Gómez-Jordana Switzerland 🇨🇭

Background in operations and technical systems. Currently focused on backend development and IoT automation.

GitHub: @Katolux

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If you'd like feedback, collaboration, or discussion around IoT architecture, automation logic, or applied ML in small-scale agriculture, feel free to connect.

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