Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer...

Area of Science:

• Environmental Science
• Computer Science
• Engineering

Background:

• Rapid urbanization presents significant waste management challenges for cities globally.
• Traditional manual waste classification methods are inefficient, slow, and costly.
• Automated waste management is crucial for enhancing recycling and reducing landfill waste.

Purpose of the Study:

• To develop a real-time smart waste classification model for efficient urban waste management.
• To implement a hybrid machine learning approach for accurate waste categorization.
• To improve recycling rates and minimize the environmental impact of waste.

Main Methods:

• A hybrid machine learning model combining a multilayer perceptron (MLP) and a multilayer convolutional neural network (ML-CNN) was developed.
• The MLP performed binary classification (metal vs. non-metal), while the ML-CNN classified non-metal waste types.
• A camera captured images of waste on a conveyor belt for real-time classification and automated sorting using a robotic arm.

Main Results:

• The proposed hybrid model achieved high accuracy in real-time waste classification.
• Experiments demonstrated the model's effectiveness with image segmentation in a practical setting.
• The system achieved 0.99% accuracy across training, testing, and validation datasets.

Conclusions:

• The developed real-time smart waste classification system offers an efficient and accurate solution for urban waste management.
• Automated sorting using machine learning can significantly improve recycling processes and reduce reliance on landfills.
• This technology supports sustainable urban development by addressing critical waste management challenges.