Fertile and Infertile Egg Classification Project

This project utilizes the ESP32 microcontroller along with an AI-based model to classify fertile and infertile eggs using an attached camera. The project leverages the Edge Impulse inferencing SDK for running the classification model directly on the ESP32.

Project Components

• ESP32 Board: The core microcontroller used in this project, specifically the AI Thinker model, which includes PSRAM for efficient camera processing.
• Camera Module: The ESP32 is connected to a camera module, which captures images of the eggs for classification.
• Edge Impulse Inferencing SDK: The SDK is used to run the trained machine learning model that classifies the images into fertile or infertile eggs.
• Wi-Fi Connectivity: The ESP32 connects to a Wi-Fi network, allowing users to access the classification results via a web interface.

Hardware Requirements

• ESP32 AI Thinker Board
• Camera module compatible with the ESP32 (AI Thinker model)
• USB Cable for programming and power
• Wi-Fi network for connecting the ESP32

Software Requirements

• Arduino IDE with ESP32 board support
• Edge Impulse SDK installed and configured
• Wi-Fi credentials for connecting the ESP32 to a local network

Project Setup

1. Hardware Setup:

   • Connect the camera module to the ESP32 according to the pin configuration defined in the code.
   • Ensure that the ESP32 board is connected to your computer via USB.

2. Software Setup:

   • Install the necessary libraries in the Arduino IDE:
     • esp_camera
     • WiFi
     • ESPAsyncWebServer
     • AsyncTCP
     • Edge Impulse Inferencing SDK
   • Configure the Wi-Fi credentials in the code:

     const char* ssid = "Your_SSID";
     const char* password = "Your_Password";

   • Load the project code into the Arduino IDE and upload it to the ESP32 board.

3. Edge Impulse Model:

   • Train a model using the Edge Impulse platform for classifying fertile and infertile eggs.
   • Deploy the trained model to the ESP32 by exporting the inference code from Edge Impulse and integrating it with the project.

Running the Project

1. Power the ESP32: Once the code is uploaded, power the ESP32 via USB or an external power source.
2. Connect to Wi-Fi: The ESP32 will automatically connect to the Wi-Fi network specified in the code. Monitor the Serial output for connection status and IP address.
3. Access the Web Interface:
   • Open a web browser and enter the IP address of the ESP32 to access the object detection results.
   • The webpage will dynamically update with the classification results as the ESP32 processes images.

Code Overview

• Camera Configuration: The camera is initialized with specific pins and settings for the AI Thinker model.
• Wi-Fi Setup: The ESP32 connects to the specified Wi-Fi network and hosts a web server.
• Image Capture and Processing: The camera captures images, which are then processed and passed to the Edge Impulse model for classification.
• WebSocket Server: The ESP32 uses WebSocket to update the web interface in real-time with the classification results.

Troubleshooting

• Camera Initialization Failed: Ensure the camera module is correctly connected and the pin configuration matches your setup.
• Wi-Fi Connection Issues: Double-check the SSID and password. Verify that the Wi-Fi network is within range.
• Web Interface Not Loading: Make sure the ESP32 is connected to the Wi-Fi, and the correct IP address is used.

Future Enhancements

• Add more categories for classification.
• Implement anomaly detection to identify unclear or invalid images.
• Improve the web interface with more detailed result displays and user controls.

License

This project is licensed under the MIT License. Feel free to modify and distribute as needed.

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This README provides an overview of setting up and running the Fertile and Infertile Egg Classification project using ESP32. Follow the instructions carefully to ensure proper functionality and successful classification.