# GUARDEN - Garden Urban AI Rodent Detection and Environment Node

<div align="center" data-with-frame="true"><figure><picture><source srcset="https://2390122264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FDNEvVRIapYE7yS03Fx5e%2Fuploads%2Fgit-blob-b5131db8eaabe76029e3e39ecb2466764de07397%2Fguarden_dark.png?alt=media" media="(prefers-color-scheme: dark)"><img src="https://2390122264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FDNEvVRIapYE7yS03Fx5e%2Fuploads%2Fgit-blob-60e32db5b44e4839ad8757eb517ac5976d0a4d50%2Fguarden.png?alt=media" alt="" width="375"></picture><figcaption><p>image [1] logo</p></figcaption></figure></div>

<p align="center"><strong>Seattle University - College of Science and Engineering</strong><br><br><strong>ECEGR 4640: Internet of Things (IoT)</strong></p>

<p align="center">Ezekiel A. Mitchell<br>emitchell4@seattleu.edu<br><br>Danny Woo Community Garden (Andy Allen, aallen@interimcda.org)</p>

<p align="center"><a href="https://github.com/ezekielamitchell/GUARDEN/blob/main/LICENSE/README.md"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"></a> <a href="https://www.espressif.com/en/products/socs/esp32-c3"><img src="https://img.shields.io/badge/platform-ESP32--C3-green.svg" alt="Platform"></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="Python"></a></p>

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## Overview

The Danny Woo Community Garden (established 1975) is a 1.5-acre urban P-Patch serving Seattle's International District/Chinatown community. Rat infestations significantly reduce harvest yields. GUARDEN offers a data-driven solution utilizing distributed AI-powered camera nodes to monitor and map rat activity, facilitating targeted pest control.

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## Key Features

* **Edge AI Detection** - On-device TensorFlow Lite inference on ESP32-C3
* **Low Power Operation** - Motion-triggered wake with deep sleep (10µA standby)
* **Privacy-First** - Metadata-only transmission, no images leave devices
* **Real-Time Monitoring** - Local web dashboard with live visualization
* **Data-Driven Insights** - Activity heatmaps, peak detection times, nesting area identification
* **Offline Operation** - Fully self-contained system with no internet dependency
* **Local Network** - Pi 5B+ acts as a WiFi access point for ESP32 nodes and user access

***

## Technical Specifications

<table data-full-width="true"><thead><tr><th>Component</th><th>Specification</th><th>Performance</th><th>Power</th></tr></thead><tbody><tr><td><strong>Processor</strong></td><td>ESP32-C3 RISC-V @ 160MHz</td><td>&#x3C;1s inference target</td><td>240mA active</td></tr><tr><td><strong>Camera</strong></td><td>OV2640 2MP sensor</td><td>96×96 grayscale capture</td><td>80mA</td></tr><tr><td><strong>AI Model</strong></td><td>MobileNetV2 α=0.35 TFLite INT8</td><td>>65% accuracy target</td><td>160mA</td></tr><tr><td><strong>Motion Sensor</strong></td><td>PIR sensor</td><td>Event-driven wake</td><td>50µA standby</td></tr><tr><td><strong>Sleep Mode</strong></td><td>Deep sleep</td><td>Extended battery</td><td>10µA</td></tr><tr><td><strong>Connectivity</strong></td><td>WiFi 802.11 b/g/n + MQTT</td><td>Local AP only</td><td>120mA TX</td></tr><tr><td><strong>Storage</strong></td><td>MicroSD + PostgreSQL</td><td>Offline resilience</td><td>20mA write</td></tr><tr><td><strong>Power</strong></td><td>Solar + 5000mAh LiPo</td><td>10+ day runtime</td><td>Self-sustaining</td></tr></tbody></table>

***

## Performance Targets

<table><thead><tr><th>Metric</th><th>Target</th><th>Status<select><option value="sqgq1MesM8XL" label="Achieved" color="blue"></option><option value="83KQpvtBIYxJ" label="In progress" color="blue"></option></select></th></tr></thead><tbody><tr><td>Detection Latency</td><td>&#x3C;1s</td><td><span data-option="83KQpvtBIYxJ">In progress</span></td></tr><tr><td>Battery Life</td><td>3+ days</td><td><span data-option="83KQpvtBIYxJ">In progress</span></td></tr><tr><td>Detection Accuracy</td><td>>65%</td><td><span data-option="83KQpvtBIYxJ">In progress</span></td></tr><tr><td>Network Uptime</td><td>>20%</td><td><span data-option="83KQpvtBIYxJ">In progress</span></td></tr><tr><td>Storage Capacity</td><td>3+ days</td><td><span data-option="83KQpvtBIYxJ">In progress</span></td></tr></tbody></table>

***

## Project Structure

```
GUARDEN/
├── edge/                        # ESP32-C3 firmware (PlatformIO)
│   ├── platformio.ini
│   └── src/
│       ├── main.cpp             # Entry point, deep sleep logic
│       ├── camera.cpp/.h        # OV2640 init + capture
│       ├── detector.cpp/.h      # TFLite inference wrapper
│       ├── mqtt_client.cpp/.h   # WiFi + MQTT publish
│       ├── model_data.h         # Auto-generated from quantize.py
│       └── config.h             # Pin defs, thresholds, node ID
│
├── fog/                         # Raspberry Pi hub (all services)
│   ├── broker/mosquitto.conf    # MQTT broker config
│   ├── ingestion/main.py        # MQTT → PostgreSQL ingestion
│   ├── dashboard/app.py         # Flask dashboard + API
│   ├── db/schema.sql            # Database schema
│   ├── setup.sh                 # One-shot Pi setup script
│   └── requirements.txt
│
├── model/                       # ML pipeline (run on laptop)
│   ├── train.py                 # MobileNetV2 training
│   ├── quantize.py              # INT8 TFLite export
│   ├── evaluate.py              # Accuracy benchmarks
│   └── guarden_v1_int8.tflite   # Trained model
│
├── config/
│   ├── nodes.yaml               # Node locations + GPS coords
│   └── .env.example             # Environment variable template
│
└── tests/
    ├── test_ingestion.py
    ├── test_dashboard.py
    └── test_model.py
```