## 2 Related Works
### AI and Machine Learning in Plastic Recycling  
Multiple studies focusing on waste management challenges due to pollution and inadequate solid waste classification underscore the growing significance of environmental protection. Various research efforts emphasize the application of advanced technologies, particularly AI and machine learning (ML), to improve recycling processes, such as:  
* Automated Sorting Systems utilize computer vision and convolutional neural networks (CNNs) to identify and classify recyclable materials. For instance, one system achieved 95% accuracy in sorting plastics, 96% in sorting glass and metal, and 94% in sorting cardboard [53]. Another piece of research highlights a high level of performance in regard to identifying plastics, with 95.67% accuracy, based on a test set, using deep learning algorithms [54; 55];
* The integration of sensors and robotics, designed to assist human workers and optimize sorting processes. For example, one proposal includes an innovative plastic waste separation system, utilizing six sensors for effective material sorting [56];
* Data-driven decision making outlines the need for a continuous lifecycle and cost analysis to evaluate the ecological and economic impacts of recycling [57]. An estimated 150,000 images are also required to improve detection accuracy in robotic sorting systems [53].  
Although numerous solutions focus on using deep learning (DL) for plastic identification [58], none have been implemented at an industrial scale. Most approaches are still in the research or experimental phase, with challenges such as cost, scalability, and integration into existing recycling systems preventing their widespread adoption.  
[PERSON] et al. [59] present a novel DL approach for waste sorting that utilizes multiple auto-encoders to extract spatio-spectral information from Red-Green-Blue (RGB) and multispectral sensors. The proposed system achieves a 99.5% mean average precision for waste localization and 96% classification accuracy across various waste types, including plastics. [PERSON] et al. [60] focus on low-value recyclable waste, utilizing a combination of RGB and limited NIR data for cost-effective sorting. The fusion model achieved a comprehensive F1-score of 0.961 for material and color recognition, addressing the challenges associated with sorting small-sized and diverse waste streams.  
[PERSON] et al. [61] introduce an IoT-based strategy for generating automatic deep learning models, addressing challenges in dataset collection and optimization. By employing an Android application for image capture and cloud processing, the system ensures the creation of an accurate dataset with 99.86% accuracy. The approach reduces training time by 9-28%, demonstrating its effectiveness in recycling applications.  
[PERSON] et al. [62] introduce the concept of AIoT, which integrates AI with IoT to enhance learning environments. AIoT refers to the integration of AI capabilities into IoT devices, enabling these interconnected systems to make intelligent decisions based on data analysis and real-time inputs from their environment.  
Analyzing the literature reveals that the majority of works focus on industrial recycling. This is a major drawback of the proposed approaches, as recycling initiatives must start from the direct consumer. The fact that automatic sorting systems rely on sophisticated equipment translates into high costs, which limit their applicability in areas with limited budgets, particularly at the residential consumer level. This aspect makes progress in the field of recycling moderate. Therefore, this paper aims to streamline the recycling process by proposing a solution implemented at the community level, with low costs.