Eco-Detect: A Sustainable Solution to Advanced Waste Sorting using AI and IoT

The traditional waste management system, prevalent in many regions, relies heavily on manual sorting processes carried out by human workers. These processes are often labor- intensive and time-consuming, leading to inefficiencies in waste management operations. Manual sorting methods struggle to cope with the increasing volume and complexity of waste generated, resulting in challenges in accurately categorizing and classifying different types of waste materials. This limitation not only hampers the effectiveness of waste sorting but also impedes efforts to maximize recycling rates and minimize environmental impact. In response to the limitations of traditional waste management approaches, the proposed Eco Detect Advanced Waste Sorting system presents a transformative solution. Leveraging the advancements in artificial intelligence and computer vision, this system introduces the utilization of the YOLOv7 algorithm for real-time interference. YOLOv7, renowned for its exceptional accuracy and speed in object detection, is integrated into the waste sorting process to enable rapid and precise identification and classification of diverse waste materials. By employing deep learning methodologies, the system is capable of recognizing a wide array of waste categories, including plastics, paper, glass, metals, and organic materials, with remarkable efficiency. The integration of the YOLOv7 algorithm into the proposed waste sorting system represents a significant advancement in waste management technology. By enabling swift and accurate identification of various waste types, the system facilitates the optimization of recycling practices, promotes the establishment of a circular economy, and contributes to the overall sustainability agenda. Its adaptability and scalability make it well-suited for widespread implementation, addressing the pressing need for sustainable waste management for clean energy solutions on a global scale. Ultimately, the paper envisions a future where technology, particularly Yolov7, use IoT for real-time monitoring. Developing user-friendly interfaces and exploring blockchain for transparent recycling tracking would further optimize the system.