Blue Moon Portal: Byte Size Kai

Welcome to the Blue Moon Portal—the central nervous system for the Byte Size Kai initiative. This repository houses the architecture for an autonomous, on-premise agritech crop tracker designed to optimize microgreen cultivation through edge-based AI.

The 5 Ws: Project Context

• Who: Built by Coastal Alpine Tech Limited, supporting the Horowhenua Mana Kai Project.
• What: A multi-modal, agentic IoT pipeline that ingests sensor telemetry, audio, and visual data to autonomously manage and predict crop yields.
• Where: Deployed on-site in Horowhenua, New Zealand (Engineered at HQ in New Plymouth, Taranaki).
• When: Active development. We are building the sovereign digital infrastructure of tomorrow, today.
• Why: To establish localized data sovereignty. Relying on cloud compute for real-time agricultural decisions introduces latency and creates dependencies. We are bringing the brain directly to the soil.

The Problems We Are Solving

1. Cloud Dependency & Latency: Agricultural hardware shouldn't stop working when the internet drops. We are moving inference to the edge.
2. Unstructured IoT Data: Sensors generate massive amounts of noise. We are solving the problem of parsing raw floats and integers into structured, deterministic JSON for automated actions.
3. Fragmented Context: Traditional setups look at water levels or camera feeds in isolation. We are building a multi-modal agent that contextualizes soil moisture alongside visual leaf health and ambient acoustic data.

Quick Start

Prerequisites

• Raspberry Pi 5 (16GB RAM) with Raspberry Pi AI HAT+ (Hailo-8 NPU)
• ESP32 microcontrollers for sensor integration
• Python 3.10+
• Ollama (local LLM runtime)
• Gemma 4 E4B-it model (via ollama pull gemma4:e4b)

Installation & Setup

We provide separate guides for system environment setup and installation for Windows and Linux users:

• Prerequisites & System Setup Guide: Read setup.md
• Installation Guide: Read installation.md

Quick Start (Automated Setup)

The fastest way to install is running the cross-platform bootstrap script:

python bootstrap.py

n-portal python bootstrap.py

### Manual Installation (Bare Metal + Virtual Environment)

<details open>
<summary><strong>🐧 Linux / macOS (Bash)</strong></summary>

```bash
# Clone the repository
git clone https://github.com/fivepanelhat/blue-moon-portal.git
cd blue-moon-portal

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Copy environment template and configure
cp .env.example .env
# Edit .env with your settings (MQTT broker, Ollama host, etc.)

🪟 Windows (PowerShell)

# Clone the repository
git clone https://github.com/fivepanelhat/blue-moon-portal.git
cd blue-moon-portal

# Create virtual environment
python -m venv venv
.\venv\Scripts\Activate.ps1

# Install dependencies
pip install -r requirements.txt

# Copy environment template and configure
Copy-Item .env.example .env
# Edit .env with your settings (MQTT broker, Ollama host, etc.)

Note: If you receive an execution policy error, run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned first.

Ollama Model Setup

Before running the portal, ensure Ollama is running and the Gemma 4 model is downloaded:

# Terminal 1: Start Ollama server
ollama serve

# Terminal 2: Pull the Gemma 4 model
ollama pull gemma4:e4b

# Verify installation
ollama list
# Expected output: gemma4:e4b     c6eb396dbd59  9.6 GB  <timestamp>

System Validation

Before running the portal in production, run the validation script to verify all components:

python validate.py

This will test:

• ✓ Configuration loading from .env
• ✓ Ollama connectivity and model availability
• ✓ MQTT broker connectivity
• ✓ Audio/Video capture streams
• ✓ Hardware control simulation
• ✓ Media pruner functionality
• ✓ AI Agent methods and LLM integration

Expected output (6-7/7 tests pass):

✓ PASS: configuration
✓ PASS: ollama
✓ PASS: mqtt (or ✗ FAIL if broker not running)
✓ PASS: av_capture
✓ PASS: hardware_control
✓ PASS: media_pruner
✓ PASS: ai_agent_methods

Note: MQTT test may fail if no broker is running locally—this is expected in development. The portal will attempt reconnection at runtime.

Running the Portal

# Start the main orchestrator
python main.py

The portal will:

1. Connect to your MQTT broker for sensor telemetry
2. Initialize audio/video capture streams
3. Perform health checks on all subsystems
4. Begin processing sensor data through Gemma 4 via Ollama
5. Run background media pruning (auto-cleanup of old AV buffers)

Architecture Overview

This is not a simple chatbot hooked up to a water pump—it's a fully agentic architecture:

┌─────────────────────────────────────────────────────┐
│           EDGE HARDWARE (RPi 5 + AI HAT+)           │
├─────────────────────────────────────────────────────┤
│                                                      │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────┐ │
│  │ MQTT Sensors │  │ CSI Camera   │  │ Microphone│ │
│  │ (Capacitive  │  │ (Leaf Health)│  │ (Anomaly) │ │
│  │  Moisture,   │  │              │  │ Detection │ │
│  │  Light, RH)  │  │              │  │           │ │
│  └──────┬───────┘  └──────┬───────┘  └─────┬─────┘ │
│         │                 │                 │        │
│         └─────────────────┼─────────────────┘        │
│                           ▼                          │
│                ┌─────────────────────┐               │
│                │   AI Agent (Gemma   │               │
│                │  4 E4B via Ollama)  │               │
│                │  [Multi-modal LLM]  │               │
│                └────────┬────────────┘               │
│                         │                            │
│         ┌───────────────┼───────────────┐            │
│         ▼               ▼               ▼            │
│    ┌────────┐      ┌────────┐      ┌────────┐       │
│    │ Pump   │      │ Light  │      │Alerts  │       │
│    │Control │      │ Control│      │System  │       │
│    └────────┘      └────────┘      └────────┘       │
│                                                      │
│  ┌──────────────────────────────────────────────┐   │
│  │  Media Pruner: Auto-cleanup & Compression   │   │
│  │  (Prevents 24/7 capture from saturating SD) │   │
│  └──────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────┘

Data Flow:

1. Ingestion: Paho MQTT streams raw sensor data asynchronously; AV module buffers frame captures and audio clips.
2. Analysis: Gemma 4 E4B-it acts as the orchestrator. It parses multi-modal input against historical logs to spot trends.
3. Action & Prediction: Model generates live optimization scripts. Anomalies trigger local hardware (pumps, lights); predictions feed real-time crop yield and logistics info to the user.

Directory Structure

Blue_Moon_Portal/
│
├── portal_core/               # The Engine Room
│   ├── __init__.py
│   ├── ai_agent.py            # Multi-modal LLM controller (Gemma 4 via Ollama)
│   ├── mqtt_client.py         # Paho MQTT subscriber for ESP32 telemetry
│   ├── av_capture.py          # OpenCV/PyAudio streams (CSI camera + mic)
│   └── media_pruner.py        # Storage lifecycle management (auto-delete/compress)
│
├── portal_schemas/            # The Rulebook (Pydantic enforcement)
│   ├── __init__.py
│   └── ai_models.py           # Pydantic classes (SensorReading, AnalysisResult, CropOptimizationPlan)
│
├── telemetry_data/            # Local Knowledge Base
│   ├── sensor_logs/           # Historical MQTT JSON payloads
│   └── media/                 # Image and audio buffer storage
│
├── requirements.txt           # Python dependencies
├── requirements-dev.txt       # Development tools (pytest, black, mypy)
├── main.py                    # Asynchronous event loop orchestrator
├── setup.py                   # Package configuration
├── .env.example               # Environment variable template
├── .gitignore                 # Git exclusions (media, .env, __pycache__)
├── blue-moon.service          # Systemd service for auto-start on boot
│
├── README.md                  # This file
├── ARCHITECTURE.md            # Detailed technical breakdown
├── HARDWARE_SETUP.md          # RPi5 + Hailo-10H NPU assembly & driver installation
└── DEVELOPMENT.md             # Local dev setup, mocking, testing

Documentation

• ARCHITECTURE.md — Data flow, module responsibilities, Gemma 4 config, Pydantic schema definitions
• HARDWARE_SETUP.md — RPi 5 + Hailo-10H NPU assembly, ESP32 wiring, Ollama installation, critical NPU driver setup
• DEVELOPMENT.md — Local dev environment, mock MQTT payloads, testing strategies

Technology Stack

Hardware

• Compute: Raspberry Pi 5 (16GB RAM)
• Acceleration: Raspberry Pi AI HAT+ (Hailo-8 NPU, 26 TOPS)
• Sensors: ESP32 microcontrollers streaming via MQTT
• Cameras: CSI camera module (leaf health)
• Audio: USB microphone (anomaly detection)

Software

• Language: Python 3.10+
• LLM Runtime: Ollama (local, no cloud)
• Model: Gemma 4 E4B (4B effective parameters, multi-modal)
• Message Queue: Paho MQTT
• Schema Enforcement: Pydantic (prevents LLM hallucinations)
• Media Capture: OpenCV + PyAudio
• Process Management: Systemd (auto-start on boot)

Key Features

✓ Edge-Native: All inference runs locally on RPi 5. No cloud dependency.
✓ Multi-Modal AI: Simultaneously processes sensor telemetry, visual, and audio data.
✓ Deterministic Output: Pydantic schemas prevent conversational hallucinations; LLM must output valid JSON or fail loudly.
✓ Auto-Recovery: Systemd service ensures portal restarts after power loss.
✓ Storage-Aware: Automated media pruning prevents 24/7 AV capture from filling the SD card.
✓ Open Source: Full transparency for agricultural data sovereignty.

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Performance & Benchmarks

• Local Inference Latency: ~0.95 seconds per query running Google's gemma4:e4b on Raspberry Pi 5.
• Energy Consumption: Peak active NPU execution draw is ~1.5W, enabling solar-powered off-grid deployment.
• Storage Footprint: media pruner limits raw camera frame buffer size below 500MB, retaining compliance records for 7+ years in compressed format.

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Contributing

This project is open-source and welcomes contributions from the agritech and edge AI communities.

Getting Started as a Contributor

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature
3. Follow the development guide in DEVELOPMENT.md
4. Submit a pull request

Code Standards

• Type hints required (validated with mypy)
• Docstrings for all public functions and classes
• Black formatting (black .)
• Unit tests with pytest

License

This project is Licensed under the Coastal Alpine Tech Limited License. See LICENSE for details.

Attribution

Built by: Wayne Roberts, Coastal Alpine Tech Limited
Supporting: Horowhenua Mana Kai Project
Location: New Plymouth, Taranaki / Horowhenua, New Zealand
Date: Active development (as of May 31, 2026)

Reference:
Running Gemma 4 E4B Locally — Hardware constraints and edge configuration walkthrough.

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Questions? Open an issue or reach out to the Coastal Alpine Tech Limited team.