Development of an AI-Based Classification Module Using Macroscopic and Microscopic Fungal Images
Project Coordinator: Prof. Dr. Ilgaz Akata
Project Research Team: Prof. Dr. Emre Keskin , Prof. Dr. Mehmet Serdar Güzel, Dr. Koray Açıcı, Assoc. Prof. Dr. Fatih Ekinci, Gülce Ediş , Eda Kumru
Funding provider: Ankara University BAP (Project Code: FCD-2025-3911)
Project Status: Ongoing (2025 - 2028)
An AI-powered classification system for identifying fungal species is being developed by evaluating macroscopic and scanning electron microscopy (SEM) images together. This system aims to overcome the limitations of traditional taxonomic approaches and achieve higher classification accuracy by combining morphological data at different scales.
The developed approach is supported, in particular, by deep learning-based image processing algorithms. Through structures such as convolutional neural networks (CNNs), numerous macroscopic and microscopic images of different species are modeled; thus, subtle morphological differences between species can be distinguished with high accuracy.
This method has been shown to produce both faster and more objective results compared to classical methods. Furthermore, it has the potential to standardize the classification process by reducing the need for expert interpretation. This AI-based system is expected to become a widely used tool not only in the field of systematic mycology but also in applied fields such as food safety, agricultural biotechnology, and environmental biomonitoring.
