Advanced deep learning approaches for the automated classification of macrofungal species in biodiversity monitoring

Abstract

Macrofungal species attract significant attention due to their critical roles in ecosystems and widespread industrial applications. Traditional species identification methods are expertise-intensive and time-consuming processes. Artificial intelligence (AI) techniques, especially, deep learning (DL), have been employed to accelerate these processes and improve result accuracy. This article aimed to classify five macrofungi using AI, specifically DL. The study focuses on classifying Amanita muscaria, A. phalloides, Lepista nuda, Macrolepiota procera, and Craterellus cornucopioides, utilizing various DL models, including DenseNet121, InceptionV3, MobileNetV2, Xception, VGG16, and ResNet101. The dataset comprised 683 images across five classes. The data were collected in a balanced manner, and the model’s effectiveness was evaluated based on accuracy, precision, recall, and F1-score metrics. Additionally, Grad-CAM visualizations were utilized to analyze the regions of focus. The best-performing model achieved 93% accuracy (7% error), outperforming a simple Convolutional Neural Network baseline with 70% accuracy (30% error). Overall, all transfer-learning models achieved accuracies of ≥ 90%. In particular, the DenseNet121 and Xception models achieved the maximum success by correctly identifying relevant regions of these species. The study demonstrates that AI, particularly DL-based techniques, can be effectively applied in species identification. Expanding datasets could further enhance their performance. The novelty of this study is the use of a combination of transfer-learning and Grad-CAM explainability to provide an interpretable and biologically meaningful framework for macrofungi identification.