AI-Powered Waste Classification Using Convolutional Neural Networks (CNNs)

In Malaysia, approximately 70%-80% of recyclable materials end up in landfills due to low participation in Separation at Source Initiative. This is largely attributed to the public perception that waste segregation is a foreign idea, coupled with a lack of public knowledge. Around 72.19% of the residents are unsure about waste categorization and proper waste disposal. This confusion leads to apathy toward recycling efforts exacerbated by deficient environmental awareness. Existing waste classification systems mainly rely on manual entry of waste item names, leading to inaccuracies and lack of user engagement, prompting a shift towards advanced deep learning models. Moreover, current systems often fail to provide comprehensive disposal guidelines, leaving users uninformed. This project addresses the gap by specifically developing an AI- Powered Waste Classification System incorporated with Convolutional Neural Network (CNN), classifying waste technologically and providing environmentally friendly disposal guidelines. By leveraging primary and secondary waste image data, the project achieves a training accuracy of 80.66% and a validation accuracy of 77.62% in waste classification. The uniqueness of this project lies in its utilization of CNN within a user-friendly web interface that allows the user to capture or upload waste image, offering immediate waste classification results and sustainable waste disposal guidelines. It also enables users to locate recycling centers and access the dashboard. This system represents an ongoing effort to educate people and contribute to the field of waste management. It promotes Sustainability Development Goal (SDG) 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), contributes zero waste, raises environmental awareness, and aligns with Malaysia's goals to increase recycling rates to 40% and reduce waste sent to landfills by 2025.