• Nishanth Kannan S
• Surya S
• Manikandan S
• Vasanth P

Efficient waste management remains a critical concern in modern urban environments, with improper segregation of biodegradable and non-biodegradable waste leading to serious environmental and health hazards. Manual classification systems often suffer from inaccuracies and inefficiencies, necessitating the development of intelligent automation solutions. This project presents a deep learning-based system that employs Convolutional Neural Networks (CNNs) to classify waste images as biodegradable or non-biodegradable. The system enables users to upload images via a web or mobile interface, which are then processed in real time to predict the correct waste category. The model is trained on a diverse dataset of waste images, enabling it to extract key visual features such as texture, shape, and color for accurate classification. By integrating this system into existing municipal and household waste management frameworks, the project aims to improve recycling efficiency, minimize landfill usage, and promote sustainable environmental practices. This AI-driven approach offers a scalable, user-friendly, and ecoconscious solution to one of the most pressing challenges in waste management today.

Convolutional Neural Network (CNN), Waste Classification, Biodegradable Waste, Non-Biodegradable Waste, Deep Learning, Image Processing, Environmental Sustainability, Smart Waste Management, Python, Artificial Intelligence (AI), Real-Time Prediction, Waste Segregation, Eco-Friendly Technology, Automated Detection, Machine Learning.____

"Waste Classification System Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.d959-d963, May-2025, Available :http://www.jetir.org/papers/JETIR2505460.pdf

"Waste Classification System Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppd959-d963, May-2025, Available at : http://www.jetir.org/papers/JETIR2505460.pdf