Research Keyword: Transfer Learning

A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species

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Researchers developed an artificial intelligence system to automatically identify 14 different types of cup fungi (Discomycetes) from photographs. Using a method called EfficientNet combined with explainable AI, the system achieved 97% accuracy in species identification. The technology could help scientists quickly and accurately catalog fungal biodiversity for conservation efforts and ecological studies without requiring expert mycologists to examine every specimen.

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Hybrid Deep Learning Framework for High-Accuracy Classification of Morphologically Similar Puffball Species Using CNN and Transformer Architectures

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Scientists developed an artificial intelligence system that can automatically identify eight different types of puffball mushrooms from photographs with 95% accuracy. The study compared five different AI models and found that a modern convolutional neural network called ConvNeXt-Base was the best at telling apart puffball species that look very similar to each other. This technology could help amateur mushroom enthusiasts, researchers, and nature conservationists accurately identify these fungi without needing a microscope or laboratory tests.

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