Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by delimiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society.

Inadequate Municipal Solid Waste (MSW) management constitutes a critical environmental challenge, as approximately 40% of waste reaches uncontrolled disposal sites where open-air incineration generates significant air, soil, and water pollution. The objective of this study was to evaluate the MSW Environmental Management System (EMS) in Chilpancingo de los Bravo, Guerrero, Mexico, through sustainability indicators and applicable Mexican environmental regulations to identify operational and structural deficiencies that guide a comprehensive improvement in its management. The methodology comprised an analysis of the EMS via the Municipal Development Plan, the identification of environmental indicators and applicable Mexican standards, and an evaluation of the EMS through waste characterization and sustainability metrics. A sample of 208 kg was defined in accordance with standards NMX-AA-015-1985 and NMX-AA-022-1985. The results indicate a generation rate of approximately 350 tons per day (1.2 kg/capita/day), with municipal collection coverage of 70% of the territory across 24 daily routes operated by 30 vehicles. Indicators revealed a recycling rate of 4.86%, collection coverage of 79.66%, a 0% treatment rate due to the absence of composting or material recovery facilities, and 95% of waste directed to the Final Disposal Site (FDS). These findings demonstrate substantial deficiencies in the current EMS, highlighting that the systematic application of indicators is an effective diagnostic tool for identifying gaps and guiding evidence-based improvements in MSW governance.