TY - JOUR
T1 - Deep Learning-Based Classification of Macrofungi
T2 - Comparative Analysis of Advanced Models for Accurate Fungi Identification
AU - Ozsari, Sifa
AU - Kumru, Eda
AU - Ekinci, Fatih
AU - Akata, Ilgaz
AU - Guzel, Mehmet Serdar
AU - Acici, Koray
AU - Ozcan, Eray
AU - Asuroglu, Tunc
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/11/9
Y1 - 2024/11/9
N2 - 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.
AB - 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.
KW - deep learning
KW - DenseNet121
KW - fungi identification
KW - machine learning models
KW - macrofungi classification
KW - Fungi/classification
KW - Deep Learning
UR - http://www.scopus.com/inward/record.url?scp=85210251689&partnerID=8YFLogxK
U2 - 10.3390/s24227189
DO - 10.3390/s24227189
M3 - Article
C2 - 39598966
AN - SCOPUS:85210251689
SN - 1424-8220
VL - 24
JO - Sensors
JF - Sensors
IS - 22
M1 - 7189
ER -