Research Keyword: Fungal classification

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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Deep Ensemble Learning and Explainable AI for Multi-Class Classification of Earthstar Fungal Species

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Scientists created an artificial intelligence system that can identify eight types of earthstar mushrooms from photographs with over 96% accuracy. These mushrooms look very similar to each other, making them difficult to tell apart by eye alone. The AI system not only identifies the mushrooms correctly but also shows which parts of the image it looked at to make its decision, making it transparent and trustworthy. This technology could help scientists monitor wild mushroom populations and improve sustainable farming practices.

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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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