Akademik Veri Yönetim Sistemi
BIOLOGY, cilt.14, ss.1-39, 2025 (SCI-Expanded, Scopus)
Accurate identification of wild
edible macrofungi is essential for biodiversity conservation, food safety, and
ecological sustainability, yet remains challenging due to the morphological
similarity between edible and toxic species. In this study, a curated dataset
of 24 wild edible macrofungi species was analyzed using six state-of-the-art
convolutional neural networks (CNNs) and four ensemble configurations,
benchmarked across eight evaluation metrics. Among individual models,
EfficientNetB0 achieved the highest performance (95.55% accuracy), whereas
MobileNetV3-L underperformed (90.55%). Pairwise ensembles yielded inconsistent
improvements, highlighting the importance of architectural complementarity.
Notably, the proposed Combination Model, integrating EfficientNetB0, ResNet50,
and Reg- NetY through a hierarchical voting strategy, achieved the best results
with 97.36% accuracy, 0.9996 AUC, and 0.9725 MCC, surpassing all other models.
To enhance interpretability, explainable AI (XAI) methods Grad-CAM, Eigen-CAM,
and LIME were employed, consistently revealing biologically meaningful regions
and transforming the framework into a transparent decision-support tool. These
findings establish a robust and scalable paradigm for fine-grained fungal
classification, demonstrating that carefully engineered ensemble learning
combined with XAI not only advances mycological research but also paves the way
for broader applications in plant recognition, spore analysis, and large-scale
vegetation monitoring from satellite imagery.