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As the world's population expands and consumption rises, urban waste continues to pile up, putting pressure on conventional waste management systems. This underscores the critical necessity for efficient recycling methods. Traditional approaches, which typically rely on extensive manual sorting, are not only ineffective but also neglect the mounting environmental challenges such as pollution and the depletion of resources. This paper presents a novel Smart Recycling Bin that integrates cameras, audio detection, and various sensors with machine learning to optimize waste sorting. Designed for both home and public use, this technology adapts to different environments, ensuring versatility. The bin’s sophisticated sensor technology combined with machine learning algorithms enables it to accurately identify and categorize various waste types, offering a scalable solution for enhancing recycling rates and reducing contamination. This advancement represents a significant step forward in making recycling more efficient and adaptable to varying waste challenges.

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Design and Implementation of a Smart Recycling Bin Using Multi-sensor Integration and Machine Learning

  • 章节

  • 发表日期：

    2026年

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  • Ioannis Gkini,
  • Griseld Gkini,
  • Dimitrios Tsipianitis

摘要

As the world's population expands and consumption rises, urban waste continues to pile up, putting pressure on conventional waste management systems. This underscores the critical necessity for efficient recycling methods. Traditional approaches, which typically rely on extensive manual sorting, are not only ineffective but also neglect the mounting environmental challenges such as pollution and the depletion of resources. This paper presents a novel Smart Recycling Bin that integrates cameras, audio detection, and various sensors with machine learning to optimize waste sorting. Designed for both home and public use, this technology adapts to different environments, ensuring versatility. The bin’s sophisticated sensor technology combined with machine learning algorithms enables it to accurately identify and categorize various waste types, offering a scalable solution for enhancing recycling rates and reducing contamination. This advancement represents a significant step forward in making recycling more efficient and adaptable to varying waste challenges.