Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review
Abstract
The intersection of artificial intelligence and environmental sustainability has become a relevant exploration domain in the contemporary era of rapid technological advancements and complex global challenges. This work reviews the application of machine learning (ML) models to address the pressing issue of plastic waste (PW) management. By systematically examining the state of the art with snowballing, this research aims to determine the efficiency and effectiveness of ML-based methods for PW detection and classification. Considering the increasing environmental concerns and information processing potential, this article hypothesised that ML models could contribute to more sustainable PW management practices. For this purpose, two scientific article repositories were examined from 2000 to 2023, and 188 articles were identified. After the systematic screening procedure, 28 were selected. Additionally, 28 more articles were included by snowballing. It was observed that accuracy in either detection or classification problems often exceeded the 80% detection accuracy benchmark, further improving when the model combination was employed. As a result, strong support was reached for the applicable potential of ML in PW. It was also concluded that models based on convolutional neural networks were the most commonly used.
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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