# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 1 Introduction and Background
### Sensor-Based Systems to AI: Advancing Waste Sorting
Their findings indicate that public participation, knowledge, and attitudes play an important role in waste sorting behavior, highlighting how user interaction with AI-powered systems can promote greater recycling efficacy in public contexts. In large-scale applications, [PERSON] et al. [8] investigated the use of robotic AI systems in municipal waste sorting facilities to replace or supplement manual sorting. Their research demonstrated improvements in recycling rates and the purity of sorted materials, which highlights the potential scalability of robotic AI solutions in industrial environments. This study serves as a valuable reference for the scalability and operational efficiency that our proposed prototype intends to achieve. Further advancing this domain, [PERSON] et al. [15] presented an AI-based digital model customized for smart cities, designed to automatically classify waste according to the principles of the circular economy. Using an artificial neural network (ANN) and feature fusion techniques, their model achieved an accuracy of 91.7% in various waste categories, showcasing the role of AI in the solution to urban waste challenges and meeting recycling requirements. These studies collectively establish a foundation for the integration of AI in automated waste sorting, supporting the design and scalability of sustainable solutions in diverse settings. Convolutional Neural Networks (CNNs) have demonstrated a significant level of effectiveness in the domain of image classification tasks. This achievement is largely due to their ability to learn intricate patterns and features from extensive datasets, enabling them to generalize and accurately identify new and previously unseen objects. These systems, such as RecycleNet, using Mask R-CNN algorithms [16], achieved a 90.2% accuracy in the classification of recyclable materials, including aluminum, paper, PET bottles, and nylon [17]. This resulted in improved recycling efficiency by automating the processes for classifying and retrieving materials. These systems have been successfully implemented in a range of applications, including industrial automation and recycling tasks. Various research works in the literature have highlighted the application of CNNs for waste management systems. [PERSON] et al. [18] presented enhanced CNN-based object detectors for municipal waste detection on a single autonomous robotic system, achieving an accuracy of 97.63% with SSD and 95.76% with Faster R-CNN. [PERSON] et al. [19] introduced a hybrid ensemble CNN model boosted by Multi-Scale Local Binary Pattern (MLBP) and Simulated Annealing (SA) for optimization, significantly enhancing accuracy to 99.01% on TrashNet and 99.41% on HGCD datasets. [PERSON] et al. [20] developed an enhanced waste management system leveraging a CNN-based image classifier to automate waste sorting, resulting in notable efficiency and cost reductions. Their model outperformed traditional models such as AlexNet, VGG16, and ResNet34, with a classification accuracy of 94.53%. [PERSON] et al. [21] employed a CNN-based method with transfer learning to categorize recyclable materials, achieving 95% accuracy for categories such as plastic, paper, and metal. [PERSON] et al. [22] introduced a vision transformer-based method that enhances waste classification by utilizing self-attention mechanisms, achieving a top accuracy of 96.98% on the TrashNet dataset and outperforming traditional CNNs by effectively addressing global information within images. [PERSON] et al. [23] presented the Self-Adaptive Waste Management System (SAWMS), which employs CNNs and conveyor systems for real-time sorting. It is capable of adapting and self-training to maintain accuracy with changing waste compositions. [PERSON] et al. [24] conducted a thorough review of deep learning techniques applied in waste management, pointing out current research gaps and future potential. The study highlighted the limited availability of comprehensive analyses and application-specificdatasets for image classification and object detection in waste tasks.