Automated Waste Segregation using Machine Learning
Waste segregation is an essential process in managing and reducing waste. With the increasing amount of waste generated globally, there is a need for efficient and automated waste segregation techniques to reduce the burden on environment. Computer vision technology has shown greater potential in waste segregation, as it can automate the process, increase accuracy, and reduce human error. Computer vision-based waste segregation uses videos of waste and computer algorithms to detect and classify different types of waste materials. The system uses machine learning techniques to train and recognize various categories of waste, including metal, plastic, e-waste, paper, and glass waste. The algorithm identifies and classifies the waste materials based on colour, texture, shape, and other visual cues. This approach has several advantages over traditional waste segregation techniques including faster processing time, reduced labour cost, and increased accuracy in identifying and classifying waste materials. It also reduces the chances of human error and ensure that the waste is sorted correctly, which can ultimately lead to improved waste management practices and a cleaner environment. This is a promising technique that helps to reduce pollution due to waste disposal and create a sustainable environment.
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