FungAI: Optimizing Fungal Cultures and Mycelium Bio-composites Production Through AI Monitoring

Fungi are playing an increasingly important role in biotechnology, with applications ranging from sustainable materials to pigment and enzyme production. Yet two critical challenges continue to limit their broader adoption. On one hand, mycelium bio-composites—materials grown from fungal mycelium—offer a promising sustainable alternative to synthetic packaging and insulation, but their growth is difficult to control, often leading to inconsistent quality. On the other hand, identifying and characterizing fungal strains still relies on costly and time-consuming molecular tools, which are not always accessible in applied settings.
FungAI aims to tackle both challenges by developing advanced artificial intelligence (AI) models that analyze fungal growth visually. One part of the project focuses on Petri dish cultures, using image data to identify species-specific traits, detect contamination, assess colonization dynamics, and even predict the production of pigments. The other part monitors the development of mycelium bio-composites during incubation, linking visual growth patterns to physical properties such as strength, moisture resistance, and thermal insulation.
Building on the results of an earlier feasibility study, this phase is dedicated to validating the technical feasibility of the proposed AI-based approaches. It includes the creation of a structured dataset by producing and testing fungal materials and fungal cultures under varied conditions, and by linking visual data with mechanical and biological test results. The project is carried out by Avans Hogeschool in collaboration with industry and academic knowledge partners, combining expertise in fungal biology, mycelium materials, and AI-driven solutions. This foundation will support the next phase of AI model training and deployment.