## 2 Related Works
### ML Applied in Waste Classification  
[PERSON] et al. [4] investigate the maturity of data analytics in sensor-based and robot sorting in Austrian waste management. Expert interviews with stakeholders across the sensor sorting value chain highlighted varying levels of data maturity. The findings indicate that larger companies tend to have higher maturity levels in data analytics, with sensor and sorting machine manufacturers leading the way. ML is the primary technology in municipal solid waste management (MSWM), aiding in tasks from waste generation prediction to treatment and disposal [5]. ML algorithms, including artificial neural networks (ANNs), support vector machines (SVMs), and deep learning (DL) models, like convolutional neural networks (CNNs), optimize waste collection, classification, and recycling. ML tools are widely used in industry evolution [6,7]. [PERSON] et al. [5] mention that waste sorting problems are treated in the literature using ML methods and forecasting. The authors note that challenges such as insufficient data, model interpretability, and integration need improvements in data sharing and real-time decision making for waste management. The classification problem is reduced to object identification based on image analyses [8], even when AI tools are involved in the process [9].  
[PERSON] and [PERSON] [10] evaluate waste treatment systems using a fuzzy logic-based multi-criteria approach. It reviews existing methods, identifies gaps, and presents a model for municipal waste sorting. The model was applied to the Wroclaw sorting facility. The prototype presented by [PERSON] and [PERSON] [11] focuses on sorting trash using a robotic arm, ML, and computer vision (CV). The object is recognized using a camera image acquisition, followed by a classification process for identifying if the object is recyclable or non-recyclable. [PERSON] and [PERSON]'s [11] research emphasizes the ML challenges related to object sizes and weights. Experiments revealed accuracy issues with glass and challenges handling larger items. The use of AI-based robotic sorting systems in municipal waste management, focusing on the ZRR2 robot installed at the Ecorparc4 sorting plant in Barcelona, is presented in the paper by [PERSON] et al. [12]. The robot, equipped with various sensors and DL algorithms, was trained to sort 13 types of waste. The results showed a sorting purity of up to 97%, with challenges such as the varying composition and morphology of municipal waste affecting recovery efficiency.  
[PERSON] and [PERSON] [13] examine the strengths and weaknesses of automated plastic waste sorting technologies by comparing manufacturers' efficiency claims with the actual sorting results at Material Recovery Facilities (MRFs). The study identifies persistent challenges such as sorting tangled materials, black plastics, and mixed-material types. [PERSON] et al. [14] present a smart trash bin concept. It integrates sensors, voice modules, and Arduino-controlled systems to measure capacity, humidity, and waste type.  
[PERSON] et al. [15] examine waste management on the island of Corfu. The island has waste sorting issues because of tourist behavior. The authorities outline the need for automatic separation to improve waste management. Inefficient waste handling was seen as detrimental to Corfu's image as a tourist destination, although stakeholders believe the issue is manageable with proper infrastructure and collaboration.