Title:
Automated Waste Sorting System Using Image Processing and Machine Learning
Abstract
Background:
Waste management is a critical issue in modern urban environments, with improper waste sorting leading to environmental pollution and inefficient recycling processes. Traditional manual waste sorting is time-consuming and prone to human error. Automating the process can improve efficiency and reduce waste sent to landfills.
Objective:
The objective of this research is to design and implement an automated waste sorting system using image processing and machine learning techniques. The system aims to accurately classify waste into recyclable, organic, and non-recyclable categories to improve recycling efficiency.
Methods:
The system uses a camera to capture images of waste items on a conveyor belt. Image processing techniques, such as feature extraction and edge detection, are applied to analyze the items. A machine learning algorithm, trained on a dataset of labeled waste images, is then used to classify the waste into different categories. The system was tested with a dataset of 5,000 waste items to evaluate its accuracy and efficiency.
Results:
The automated waste sorting system achieved a classification accuracy of 92%, with recyclable materials identified with 95% accuracy, organic waste with 89%, and non-recyclable waste with 90%. The system demonstrated the ability to sort waste in real-time, significantly reducing the need for manual labor.
Conclusion:
The automated waste sorting system using image processing and machine learning offers an effective solution for improving waste management efficiency. By reducing human error and increasing sorting accuracy, this system can contribute to more sustainable waste management practices and enhance recycling rates in urban areas.