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Turkish Journal of Botany

Abstract

In this study, deep learning (DL)-based models were developed for the classification of 5 fungal species from the Phallaceae family (Clathrus ruber, Colus hirudinosus, Mutinus caninus, Phallus impudicus, and Pseudocolus fusiformis). ConvNeXT achieved the highest performance with 98% accuracy, 98% precision, 98% recall, and 98% F1-score. EfficientNetB4 and Xception also performed well with 96% accuracy. In contrast, lighter models such as MobileNetV2 and MixNet S showed significantly lower accuracy (84% and 80%, respectively). Among the explainable artificial intelligence (XAI) techniques, gradient-weighted class activation mapping (Grad-CAM) and Integrated Gradients showed that high-accuracy models focus more effectively on biologically meaningful regions. In particular, the ConvNeXT plus Grad-CAM combination consistently highlighted critical structural areas, such as the cap and stalk of fungi, resulting in more accurate classifications. These findings show that DL-based models offer high accuracy in classifying fungal species with complex morphological features. Furthermore, XAI techniques play a critical role in enhancing classification processes.

Author ORCID Identifier

EDA KUMRU: 0009-0000-7417-6197

FATİH EKİNCİ: 0000-0002-1011-1105

KORAY AÇICI: 0000-0002-3821-6419

ÖMER ALTINDAL: 0009-0006-3822-5039

MEHMET GÜZEL: 0000-0002-3408-0083

ILGAZ AKATA: 0000-0002-1731-1302

DOI

10.55730/1300-008X.2871

Keywords

Deep learning, explainable artificial intelligence, fungi classification, Phallaceae, Grad-CAM

First Page

388

Last Page

405

Publisher

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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