'MoldVisor': AI-Based Mold Detection Technology for Proactive Building Maintenance

Abstract

Mold growth in high-humidity environments presents persistent challenges to building integrity, maintenance costs, and occupant health, particularly in regions such as the United Arab Emirates where condensation and moisture accumulation are frequent. Existing mold detection approaches are largely reactive, relying on visual inspection or post-growth identification, which often leads to delayed intervention and increased structural and health risks. This paper presents MoldVisor, an AI-enabled mold recognition and monitoring system designed to identify early-stage mold risks before visible growth occurs. MoldVisor integrates thermal imaging, environmental sensing (humidity, temperature, volatile organic compounds), and edge AI algorithms to continuously analyze environmental patterns associated with mold development. By combining real-time data acquisition, predictive analytics, and intelligent risk assessment, the system enables proactive, condition-based building maintenance. The device architecture is informed by building maintenance requirements to ensure usability, operational efficiency, and scalability. Experimental evaluation demonstrates the potential of AI-driven sensing technologies to transform mold management from reactive detection to preventive intervention, improving building health, reducing maintenance efforts, and enhancing occupant well-being. The findings provide insights for the development of intelligent, technology-driven preventive maintenance solutions in smart buildings.

Keywords: Mold Detection, AI, Environmental Sensing, Hermal Imaging, Smart Buildings, Preventive Maintenance, High-humidity Environments, Edge Computing

DOI: 10.54941/ahfe1007901