Hack-Elite-AI-Powered-Food-Spoilage-Detection

Overview

This project aims to develop an AI-powered food spoilage detection model using transfer learning with the Image captioning and ResNet50 architecture. The model classifies images of food as either fresh or spoiled/ healty or unhealthy, providing a valuable tool for reducing food waste and enhancing food safety. Integration of Raspberry pi pico W with sensors like DHT-11, MQ135 can also be seen.

Table of Contents

• Tech Stack Used
• Installation
• Project Structure
• Video and Presentation

Tech Stack Used

• Deep Learning Framework: TensorFlow + Keras
• Pre-trained Model: ResNet50 (for transfer learning)
• Data Augmentation and Preprocessing: Keras ImageDataGenerator, OpenCV, NumPy
• Optimizer: Adam
• Model Export: HDF5 format (.h5) for saving the model
• Microcontroller: Raspberry Pi Pico or similar
• Sensors: DHT11 (temperature and humidity), MQ135 (gas sensor)
• Display: I2C LCD (16x2)
• Networking: WiFi (using network module), socket communication
• Programming Language: MicroPython
• Web Development: HTML, CSS, JavaScript for user interaction

Installation

1. Clone the repository:

   git clone https://github.com/your_username/food-spoilage-detection.git
   cd food-spoilage-detection

2. Install the required packages in each folder:

   pip install -r requirements.txt

3. For ResNet50, download the dataset and place it in the data/train directory.

Project Structure

Image-Captioning/
│
├── food_classification.ipynb
├── requirements.txt

ResNet50/
│
├── data/
│   └── train/
│       ├── fresh/
│       └── spoiled/
│
├── app.py
├── requirements.txt

WebServer-and-IOT/
│
├── Circuit Diagram.png
├── Description of Components Used
├── List of Components Required.png
├── Web Server Page.png
├── finWeb.py
├── final1.py

── README.md

Video and Presentation

Drive link for Video/Presentation: Click Here!!