Smart Waste Segregation System Using Ai-Based Image Recognition

Introduction:
In the era of rapid urbanization, waste management is one of the most critical issues faced by cities worldwide. A major problem in this domain is improper waste segregation. To address this, our project proposes a smart system that uses artificial intelligence to automatically identify and sort waste into categories such as biodegradable, non-biodegradable, and recyclable.

Objective:
To design and develop an intelligent waste segregation system using image recognition technology that can help households and municipal systems improve recycling rates and reduce human effort.

Tools and Technologies Used:

Python

TensorFlow and Keras (for training image recognition models)

OpenCV (for image processing)

Raspberry Pi (for hardware integration)

Servo motors (to operate physical bins)


Methodology:

1. Data Collection: Thousands of labeled images of different types of waste were collected and categorized.


2. Model Training: A Convolutional Neural Network (CNN) model was trained to recognize and classify waste types from images.


3. Hardware Integration: A camera connected to a Raspberry Pi captures images of waste items. Based on the classification result, the servo motor directs the item to the correct bin.


4. Testing and Accuracy: The model achieved an accuracy of over 90% on real-time test data after multiple iterations and fine-tuning.



Expected Outcome:
This system significantly reduces the risk of incorrect segregation, increases recycling efficiency, and lessens the dependency on human labor. It can be implemented in residential societies, schools, and public places for educational and practical waste management purposes.

Conclusion:
The Smart Waste Segregation System is an innovative solution that leverages AI to contribute to environmental sustainability. It is a step forward in integrating technology into our daily lives for a cleaner, greener future.