Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification

被引:1
|
作者
Ozsari, Sifa [1 ]
Kumru, Eda [2 ]
Ekinci, Fatih [3 ]
Akata, Ilgaz [4 ]
Guzel, Mehmet Serdar [1 ]
Acici, Koray [5 ]
Ozcan, Eray [1 ]
Asuroglu, Tunc [6 ,7 ]
机构
[1] Ankara Univ, Fac Engn, Dept Comp Engn, TR-06830 Ankara, Turkiye
[2] Ankara Univ, Grad Sch Nat & Appl Sci, TR-06830 Ankara, Turkiye
[3] Ankara Univ, Inst Nucl Sci, Dept Med Phys, TR-06100 Ankara, Turkiye
[4] Ankara Univ, Fac Sci, Dept Biol, TR-06100 Ankara, Turkiye
[5] Ankara Univ, Fac Engn, Dept Artificial Intelligence & Data Engn, TR-06830 Ankara, Turkiye
[6] Tampere Univ, Fac Med & Hlth Technol, Tampere 33720, Finland
[7] VTT Tech Res Ctr Finland, Tampere 33101, Finland
关键词
macrofungi classification; deep learning; DenseNet121; fungi identification; machine learning models; MUSHROOMS;
D O I
10.3390/s24227189
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum 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.
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页数:22
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