Eco Sort: AI-Driven Waste Segregation System

Eco Sort: AI-Driven Waste Segregation System

S. Nithya1, Anusuya T2, Dharini T2, Jayachandran B2, Kalaivani B2

1 Professor, 2 Student, Department of Electrical and Electronics Engineering,

Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.

   Abstract— An important environmental problem is waste mishandling, which calls for effective and automated segregation systems. An AI-driven garbage segregation system that uses deep learning and image processing techniques to separate waste into biodegradable and non-biodegradable categories is shown in this research. To achieve precise categorization based on visual cues, a Convolutional Neural Network (CNN) is trained on a variety of trash datasets. The system processes garbage photos using OpenCV to ensure accurate identification. Under many circumstances, image preprocessing methods like scaling and normalization improve model performance. Over time, the algorithm learns from new trash data, increasing the accuracy of its classifications. Effective trash disposal management is also made possible by a real-time monitoring tool that keeps track of the amount of waste in bins and updates users through an interactive interface. By integrating cloud storage, waste management authorities may access and analyze data remotely, which helps them make better decisions. Deep learning, automation, and real-time monitoring are all combined in this system to improve trash management effectiveness, encourage recycling, and support environmental sustainability.

      Keywords— Deep Learning, Image Processing, Convolutional Neural Network (CNN), OpenCV, Waste Classification and Artificial Intelligence.