---
title: "TinyML Based Bird watcher"
url: "https://maker.wiznet.io/Sunny_/projects/tinyml-based-bird-watcher-1/"
markdown_url: "https://maker.wiznet.io/Sunny_/projects/tinyml-based-bird-watcher-1/md"
type: "UCC: User Created Content"
author: "Hendra Kusumah"
author_url: "https://www.hackster.io/hendra/tinyml-based-bird-watcher-3bbb40"
editor: "WIZnet"
editor_url: "https://maker.wiznet.io/"
original_author: "Hendra Kusumah"
original_url: "https://www.hackster.io/hendra/tinyml-based-bird-watcher-3bbb40"
published: "2026-01-29"
language: "en"
likes: 0
views: 312
comments: 0
source: "WIZnet Makers (https://maker.wiznet.io/)"
---

# TinyML Based Bird watcher

> A bird detection system to monitor the activity of birds in a geodesic dome using w5300 ethernet module and stm32F4 Discovery

Original author: Hendra Kusumah (source: https://www.hackster.io/hendra/tinyml-based-bird-watcher-3bbb40)

## Article

## ![](https://maker.wiznet.io/upload/ckeditor5/408389454%5F1769764538%2Epng)

## 1. What is this project?

**TinyML-based Bird Watcher** is:

> An **AI-powered bird monitoring system** that uses TinyML to detect birds and WIZnet **W5300 Ethernet** to provide a **real-time web dashboard**.

A camera with TinyML counts birds inside an aviary.

Sensors can detect **environmental and fire-risk conditions**.

An STM32F4 + W5300 combo serves this information over a **local web server**.

---

## ![](https://maker.wiznet.io/upload/ckeditor5/408389454%5F1769764562%2Epng)

## 2. Why is it needed? (Problem)

In a bird park / aviary:

Staff need to know:

How many birds are present now

Where they tend to gather

Whether their behavior looks abnormal

Doing this **manually**:

Is labor-intensive

Cannot cover **24/7**

Makes it easy to **miss early warning signs**

👉 The project solves this by providing **automatic, continuous monitoring** using **TinyML + Ethernet IoT**.

---

## 3. How does it work? (System Architecture)

### (1) Bird detection via TinyML camera

Uses a **Grove Vision AI** camera.

A custom **TinyML object detection model** is deployed on the camera.

**Input:** live camera images

**Output:**

Number of detected birds

Confidence scores per detection

The **bird counting logic runs on the edge** inside the camera module.

---

### (2) MCU processing with STM32F4

An **STM32F4 Discovery** board receives:

Bird count results from Grove Vision AI

(Optionally) environmental sensor data

It aggregates these values into **simple status data**:

`bird_count`

`temperature`, `humidity`, `gas_level`, etc.

---

### (3) Networking & web with W5300 Ethernet

Uses a WIZnet **W5300 Ethernet controller**.

Key features:

**Hardware TCP/IP offload (TOE)**

Stable, low-latency **wired Ethernet**

STM32F4 + W5300 runs a **local HTTP web server**.

When you visit the device IP in a browser:

You see a page like: `Number of birds: X`

The page can also show sensor/environment data.

👉 No special app needed — **any browser** on the same network can monitor the birds.

---

## ![](https://maker.wiznet.io/upload/ckeditor5/408389454%5F1769764605%2Epng)

## 4. Gas-based fire detection model (extension)

The project goes beyond bird counting and can also monitor **fire risk**.

Additional sensors:

**SGP30** – gas sensor

**SHT40** – temperature & humidity

A separate **TinyML model** can analyze these readings to detect:

Sudden temperature spikes

Abnormal gas levels
→ Potential **early fire or hazard indicators**

So in one device you can monitor:

> **Bird presence + environmental safety / fire risk**
> at the same time.

---

## 5. Benefits of this system

### 1) 24/7 automatic monitoring

No need for staff to always be near the aviary.

The system continuously tracks:

Bird count

Environment status

Works **day and night** without breaks.

---

### 2) Early anomaly detection

**Behavioral anomalies:**

Birds suddenly clustering in one corner

A zone where birds stop appearing

**Environmental anomalies:**

Abnormal gas readings

Rapid temperature changes

When both bird behavior and sensor data look unusual,
the system can provide a **very early warning** of problems
(before birds die or infrastructure is damaged).

---

### 3) Data-driven management

Over time, the system can log:

Bird count trends

Temperature / humidity / gas patterns

This allows:

Better **habitat optimization**

Understanding patterns over time (season, time of day, visitor flow)

More informed decisions about environmental control and maintenance

---

### 4) Easy access via web browser

Thanks to **W5300 Ethernet + built-in web server**:

Access the dashboard from:

PC

Tablet

Smartphone

Just open a browser and enter the device’s IP.

👉 No extra software, no cloud dependency for basic monitoring.

---

## 6. Why W5300? (WIZnet angle)

In a bird park or outdoor-like environment:

**Wired Ethernet** is often more:

Reliable than Wi-Fi

Stable under interference

Secure for local infrastructure

**W5300 advantages:**

Hardware TCP/IP offload (TOE)

High throughput, low latency

Parallel interface to STM32F4

This means:

The STM32 focuses on:

TinyML results

Sensor handling

Web page generation

The W5300 handles **all heavy network tasks**.

👉 Ideal for **TinyML + real-time web monitoring** with robust wired connectivity.

---

## 7. Possible applications

Beyond bird parks, this architecture can be reused for:

Zoos and wildlife monitoring

Poultry farms, livestock barns

Smart agriculture / greenhouses

Smart city micro-monitoring nodes

Any **“count + environment + web dashboard”** scenario

Replace “bird” with **any detectable object** (animals, people, vehicles, assets),
and you can reuse the **TinyML + STM32F4 + W5300** stack almost as-is.

---

Source: https://maker.wiznet.io/Sunny_/projects/tinyml-based-bird-watcher-1/