Classification of Plastic Waste Products using Deep Learning

Plastic waste has become a significant environmental concern, necessitating innovative approaches to enhance recycling processes. Traditional methods of sorting plastic waste are labor intensive and error prone, leading to suboptimal recycling rates. This study proposes a novel solution by utilizing deep learning methods to the automated classification of plastic waste based on its composition. The proposed system employs Convolutional-Neural-Networks to analyze images regarding plastic waste, providing a precise and effective way to classify various kinds of plastics. The dataset that was utilized to train the model includes plastic items of a diverse range, making sure that the model's ability of generalizing, across the various shapes, colors, and textures. Transfer learning is implemented to leverage pre trained models, facilitating the adaptation to the specific task of plastic waste classification. The implementation of deep learning based plastic waste classification contributes to the growth of sustainable waste management practices. By automating the sorting process, the recycling industry can significantly reduce processing time, minimize errors, and increase overall recycling efficiency. This research aligns with the global goal of mitigating plastic pollution and promotes a circular economy by optimizing the reuse of plastic materials. The study's conclusions encourage further exploration of deep learning applications in waste management to address environmental challenges and promote a more sustainable future.