Plastic Waste Classification using DenseNet and CNN

Plastic waste poses a substantial environmental challenge owing to its enduring nature and ubiquitous usage, exerting profound impacts on global ecosystems. Efficient sorting of plastic waste is critical for recycling and waste management, but achieving high efficiency in this mechanism is challenging. Innovative deep learning tactics can greatly enhance the categorization of plastic waste. Cutting-edge strategies, among others Convolutional Neural Networks (CNNs) and DenseNet121 provide significant advancements in image analysis. When applied to a Wide-ranging dataset of plastic waste images, these methods enable accurate identification and categorization of various plastic wastes. The process begins with data preparation, including image augmentation and normalization, to enhance model learning. Augmentation increases the dataset's diversity, while normalization scales the data appropriately for the models. This preparation is pivotal for optimal performance. CNNs and DenseNet121 are then employed to automate the classification process, improving efficiency and accuracy. DenseNet121 is particularly impactful due to its dense connections, enhancing model efficiency and effectiveness. Integrating deep learning technologies is a transformative approach to the plastic waste crisis. These methods streamline recycling processes, reduce environmental impact, and improve waste management efficiency. Embracing these innovations is integral for addressing plastic waste challenges and promoting sustainable practices.