Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures
Puffballs, a group of macrofungi belonging to the <i>Basidiomycota</i>, pose taxonomic challenges due to their convergent morphological features, including spherical basidiocarps and similar peridial structures, which often hinder accurate species-level identification. This study propose...
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2025-07-01
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| author | Eda Kumru Güney Ugurlu Mustafa Sevindik Fatih Ekinci Mehmet Serdar Güzel Koray Acici Ilgaz Akata |
| author_facet | Eda Kumru Güney Ugurlu Mustafa Sevindik Fatih Ekinci Mehmet Serdar Güzel Koray Acici Ilgaz Akata |
| author_sort | Eda Kumru |
| collection | DOAJ |
| description | Puffballs, a group of macrofungi belonging to the <i>Basidiomycota</i>, pose taxonomic challenges due to their convergent morphological features, including spherical basidiocarps and similar peridial structures, which often hinder accurate species-level identification. This study proposes a deep learning-based classification framework for eight ecologically and taxonomically important puffball species: <i>Apioperdon pyriforme</i>, <i>Bovista plumbea</i>, <i>Bovistella utriformis</i>, <i>Lycoperdon echinatum</i>, <i>L. excipuliforme</i>, <i>L. molle</i>, <i>L. perlatum</i>, and <i>Mycenastrum corium</i>. A balanced dataset of 1600 images (200 per species) was used, divided into 70% training, 15% validation, and 15% testing. To enhance generalizability, images were augmented to simulate natural variability in orientation, lighting, and background. In this study, five different deep learning models (ConvNeXt-Base, Swin Transformer, ViT, MaxViT, EfficientNet-B3) were comparatively evaluated on a balanced dataset of eight puffball species. Among these, the ConvNeXt-Base model achieved the highest performance, with 95.41% accuracy, and proved especially effective in distinguishing morphologically similar species such as Mycenastrum corium and Lycoperdon excipuliforme. The findings demonstrate that deep learning models can serve as powerful tools for the accurate classification of visually similar fungal species. This technological approach shows promise for developing automated mushroom identification systems that support citizen science, amateur naturalists, and conservation professionals. |
| format | Article |
| id | doaj-art-ebeac2e58c63418d9b182b080bca16e2 |
| institution | Kabale University |
| issn | 2079-7737 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Biology |
| spelling | doaj-art-ebeac2e58c63418d9b182b080bca16e22025-08-20T03:32:24ZengMDPI AGBiology2079-77372025-07-0114781610.3390/biology14070816Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer ArchitecturesEda Kumru0Güney Ugurlu1Mustafa Sevindik2Fatih Ekinci3Mehmet Serdar Güzel4Koray Acici5Ilgaz Akata6Graduate School of Natural and Applied Sciences, Ankara University, Ankara 06830, TürkiyeDepartment of Computer Engineering, Faculty of Engineering, Başkent University, Ankara 06790, TürkiyeDepartment of Biology, Faculty of Engineering and Natural Sciences, Osmaniye Korkut Ata University, Osmaniye 80000, TürkiyeInstitute of Artificial Intelligence, Ankara University, Ankara 06100, TürkiyeDepartment of Computer Engineering, Faculty of Engineering, Ankara University, Ankara 06830, TürkiyeArtificial Intelligence and Data Engineering, Ankara University, Ankara 06830, TürkiyeDepartment of Biology, Faculty of Science, Ankara University, Ankara 06100, TürkiyePuffballs, a group of macrofungi belonging to the <i>Basidiomycota</i>, pose taxonomic challenges due to their convergent morphological features, including spherical basidiocarps and similar peridial structures, which often hinder accurate species-level identification. This study proposes a deep learning-based classification framework for eight ecologically and taxonomically important puffball species: <i>Apioperdon pyriforme</i>, <i>Bovista plumbea</i>, <i>Bovistella utriformis</i>, <i>Lycoperdon echinatum</i>, <i>L. excipuliforme</i>, <i>L. molle</i>, <i>L. perlatum</i>, and <i>Mycenastrum corium</i>. A balanced dataset of 1600 images (200 per species) was used, divided into 70% training, 15% validation, and 15% testing. To enhance generalizability, images were augmented to simulate natural variability in orientation, lighting, and background. In this study, five different deep learning models (ConvNeXt-Base, Swin Transformer, ViT, MaxViT, EfficientNet-B3) were comparatively evaluated on a balanced dataset of eight puffball species. Among these, the ConvNeXt-Base model achieved the highest performance, with 95.41% accuracy, and proved especially effective in distinguishing morphologically similar species such as Mycenastrum corium and Lycoperdon excipuliforme. The findings demonstrate that deep learning models can serve as powerful tools for the accurate classification of visually similar fungal species. This technological approach shows promise for developing automated mushroom identification systems that support citizen science, amateur naturalists, and conservation professionals.https://www.mdpi.com/2079-7737/14/7/816puffballdeep learningfungal classificationCNN-transformer hybridimage classification |
| spellingShingle | Eda Kumru Güney Ugurlu Mustafa Sevindik Fatih Ekinci Mehmet Serdar Güzel Koray Acici Ilgaz Akata Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures Biology puffball deep learning fungal classification CNN-transformer hybrid image classification |
| title | Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures |
| title_full | Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures |
| title_fullStr | Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures |
| title_full_unstemmed | Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures |
| title_short | Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures |
| title_sort | hybrid deep learning framework for high accuracy classification of morphologically similar puffball species using cnn and transformer architectures |
| topic | puffball deep learning fungal classification CNN-transformer hybrid image classification |
| url | https://www.mdpi.com/2079-7737/14/7/816 |
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