This systematic review identifies advances and challenges in artificial intelligence for solid waste management in rural municipalities, suggesting future research directions.
This systematic review examines the application of artificial intelligence (AI) technologies in solid waste management in rural municipalities, aiming to identify the advances, benefits, challenges, and research gaps in this emerging field. Based on the analysis of 47 scientific articles selected from the Scopus database, various technological solutions were identified, including computer vision systems, deep learning, predictive models, IoT sensors, and hybrid Edge-Cloud platforms. The results show substantial improvements in waste classification, operational planning, and energy efficiency, although significant limitations associated with technological infrastructure, data availability, and technical training in rural areas persist. The review also highlights research opportunities aimed at developing lightweight models, integrating local knowledge, and generating open and representative datasets. These findings allow us to propose strategic lines for the sustainable adoption of AI in rural environments, contributing to the digital transformation of public services with a territorial focus.
No takes yet. Share an insight, caveat, or question.
Deivis Robin Arista-López (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: