convolutional neural networks

Classification of Mycena and Marasmius Species Using Deep Learning Models: An Ecological and Taxonomic Approach

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Researchers developed an artificial intelligence system to automatically identify mushroom species from the Mycena and Marasmius groups by analyzing photographs. Using advanced computer vision and machine learning techniques, they achieved 98.9% accuracy in classification. This technology could help scientists, conservationists, and nature enthusiasts quickly identify mushroom species in the field, supporting biodiversity research and conservation efforts.

Automatic classification of fungal-fungal interactions using deep learning models

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Researchers developed a computer artificial intelligence system that can automatically analyze images of fungal interactions to identify strains that could help control harmful crop diseases. Instead of having humans manually examine thousands of fungal culture plates—a slow and subjective process—the AI system can now classify the interactions between beneficial fungi and plant pathogens with 95% accuracy. This breakthrough significantly speeds up the search for natural alternatives to synthetic pesticides, supporting sustainable agriculture and food security.

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.

Research Topic: convolutional neural networks

Classification of Mycena and Marasmius Species Using Deep Learning Models: An Ecological and Taxonomic Approach

Automatic classification of fungal-fungal interactions using deep learning models

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