Automated and Simplified Waste Segregation System Using Convolutional Neural Networks and Python
G Rajesh, M Jayanthi, Shruti S Kawale, V Sainath, S Rithish Revan, A B Gurulakshmi · 2024
This study introduces a novel strategy for waste segregation employing Convolutional Neural Networks (CNNs) and Python programming. By harnessing CNNs’ image feature extraction capabilities, the method aims to create a streamlined and automated waste segregation system. The process involves gathering image data of waste, preprocessing with CNN algorithms, and extracting features to distinguish various waste types. Statistical and machine learning algorithms are then utilized to analyze these features, facilitating precise waste classification and segregation. Python serves as the implementation platform, benefiting from its rich libraries in image processing, deep learning, and data manipulation. This approach offers flexibility and accessibility, empowering researchers and waste management professionals to craft tailored and efficient segregation algorithms.