Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as <i>Amanita pantherina</i>, <i>Boletus edulis</i>, <i>Cantharellus cibarius</i>, <i>Lactarius deliciosus</i>, &l...
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MDPI AG
2024-11-01
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| author | Sifa Ozsari Eda Kumru Fatih Ekinci Ilgaz Akata Mehmet Serdar Guzel Koray Acici Eray Ozcan Tunc Asuroglu |
| author_facet | Sifa Ozsari Eda Kumru Fatih Ekinci Ilgaz Akata Mehmet Serdar Guzel Koray Acici Eray Ozcan Tunc Asuroglu |
| author_sort | Sifa Ozsari |
| collection | DOAJ |
| description | This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as <i>Amanita pantherina</i>, <i>Boletus edulis</i>, <i>Cantharellus cibarius</i>, <i>Lactarius deliciosus</i>, <i>Pleurotus ostreatus</i> and <i>Tricholoma terreum</i> were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species. |
| format | Article |
| id | doaj-art-5debc1f1429d4e69ac210d60b167cd93 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-5debc1f1429d4e69ac210d60b167cd932025-08-20T02:04:40ZengMDPI AGSensors1424-82202024-11-012422718910.3390/s24227189Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi IdentificationSifa Ozsari0Eda Kumru1Fatih Ekinci2Ilgaz Akata3Mehmet Serdar Guzel4Koray Acici5Eray Ozcan6Tunc Asuroglu7Department of Computer Engineering, Faculty of Engineering, Ankara University, Ankara 06830, TürkiyeGraduate School of Natural and Applied Sciences, Ankara University, Ankara 06830, TürkiyeDepartment of Medical Physics, Institute of Nuclear Sciences, Ankara University, Ankara 06100, TürkiyeDepartment of Biology, Faculty of Science, Ankara University, Ankara 06100, TürkiyeDepartment of Computer Engineering, Faculty of Engineering, Ankara University, Ankara 06830, TürkiyeDepartment of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, Ankara 06830, TürkiyeDepartment of Computer Engineering, Faculty of Engineering, Ankara University, Ankara 06830, TürkiyeFaculty of Medicine and Health Technology, Tampere University, 33720 Tampere, FinlandThis study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as <i>Amanita pantherina</i>, <i>Boletus edulis</i>, <i>Cantharellus cibarius</i>, <i>Lactarius deliciosus</i>, <i>Pleurotus ostreatus</i> and <i>Tricholoma terreum</i> were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species.https://www.mdpi.com/1424-8220/24/22/7189macrofungi classificationdeep learningDenseNet121fungi identificationmachine learning models |
| spellingShingle | Sifa Ozsari Eda Kumru Fatih Ekinci Ilgaz Akata Mehmet Serdar Guzel Koray Acici Eray Ozcan Tunc Asuroglu Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification Sensors macrofungi classification deep learning DenseNet121 fungi identification machine learning models |
| title | Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification |
| title_full | Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification |
| title_fullStr | Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification |
| title_full_unstemmed | Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification |
| title_short | Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification |
| title_sort | deep learning based classification of macrofungi comparative analysis of advanced models for accurate fungi identification |
| topic | macrofungi classification deep learning DenseNet121 fungi identification machine learning models |
| url | https://www.mdpi.com/1424-8220/24/22/7189 |
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