Machine Learning, Deep Learning, and IoT for Waste Classification and Intelligent Waste Management: A PRISMA-Based Systematic Review

Le résumé fourni par la source

The rapid growth of urban populations and consumption patterns has significantly increased the volume and complexity of municipal waste streams, including recyclable, organic, hazardous, and electronic waste. Traditional waste management approaches such as manual sorting and fixed collection schedules struggle to handle the diversity and scale of modern waste systems efficiently. Consequently, recent research has explored artificial intelligence (AI), computer vision, and Internet of Things (IoT) technologies to automate waste classification and enable intelligent monitoring and management of waste infrastructures. However, the rapidly expanding literature in this domain is fragmented across different datasets, sensing platforms, and experimental settings, making it difficult to obtain a comprehensive understanding of current technological progress. To address this gap, this study conducts a systematic literature review using the PRISMA methodology, analyzing 50 rigorously selected peer-reviewed studies on automated waste classification and intelligent waste management systems. The reviewed works are examined in terms of model architectures, sensing infrastructures, datasets, and system capabilities. The analysis shows that convolutional neural network architectures such as ResNet, MobileNet, and EfficientNet, together with object detection frameworks including YOLO and Faster R-CNN, dominate current visual waste classification research; however, emerging studies increasingly explore hybrid architectures combining convolutional networks with transformer-based models to improve contextual understanding and scalability. IoT-enabled smart bins equipped with ultrasonic sensors, load cells, and environmental sensors are widely used for real-time waste monitoring. The review also reveals a recent surge of research activity, particularly in Asia, with increasing contributions from major publishers such as Elsevier and MDPI. Despite promising experimental performance, most systems remain limited to small curated datasets and pilot-scale deployments, highlighting a gap between laboratory research and real-world implementation. These findings provide a consolidated overview of current research trends and identify key challenges for developing scalable and practical intelligent waste management systems.

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