The waste that world needs

Abstract

The goal of this project is to develop a detection system that can distinguish between organic and recyclable materials in a waste stream. The system utilizes deep learning algorithms to identify different types of materials based on their physical properties. The system is able to process the images and identify the materials. The project aims to improve the efficiency and effectiveness of waste management processes by ensuring that organic materials are separated from recyclable materials, thereby reducing the amount of waste sent to landfills. The results of the project have the potential to significantly contribute to sustainable waste management practices and environmental protection efforts.

This project uses the Deep learning model to develop the software. The main advantage of using this model is that it provides feature detection that is suitable for detecting the material which is organic or not. The proposed system's accuracy was evaluated using a test dataset, achieving an overall accuracy of over 85%. The organic materials identified by the system are then separated and sent to a composting facility, while the non-compostable materials are diverted to a separate processing unit for proper recycling and disposal. This detection system offers several benefits, including reducing contamination in the composting process, promoting sustainability, and providing a reliable and automated solution for waste management. Additionally, it can be used by businesses and communities to comply with composting regulations and reduce their environmental impact.