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The pervasive utilization of plastic bottles in our daily routines has escalated environmental predicaments, notably within marine ecosystems. A considerable volume of plastic bottles is carried ashore by waves, often ensnaring coastal regions. The deleterious ramifications of plastic waste, including bottles, on coastal ecology necessitate attention. Fortunately, artificial intelligence (AI) has pervaded various domains, including environmental endeavors, proffering promising resolutions to counter these ecological quandaries effectively. This study endeavors to classify plastic bottles through image data encompassing diverse scenarios. Employing distinct AI algorithms, we preprocess the dataset and conduct this classification. Remarkably, the CNN with the heuristic model attains the highest accuracy, an impressive 99%.

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From Trash to Treasure: Assessing Deep Learning Models for Plastic Waste Identification

  • 会议论文

  • 发表日期：

    2026年

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  • Huda Mahmood Hassoon Althabeti,
  • Sefer Kurnaz

摘要

The pervasive utilization of plastic bottles in our daily routines has escalated environmental predicaments, notably within marine ecosystems. A considerable volume of plastic bottles is carried ashore by waves, often ensnaring coastal regions. The deleterious ramifications of plastic waste, including bottles, on coastal ecology necessitate attention. Fortunately, artificial intelligence (AI) has pervaded various domains, including environmental endeavors, proffering promising resolutions to counter these ecological quandaries effectively. This study endeavors to classify plastic bottles through image data encompassing diverse scenarios. Employing distinct AI algorithms, we preprocess the dataset and conduct this classification. Remarkably, the CNN with the heuristic model attains the highest accuracy, an impressive 99%.