Research Keyword: feature extraction

A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications

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Researchers have created a large collection of carefully labeled photographs of oyster mushrooms along with environmental data from the farm where they were grown. The dataset includes about 16,000 images showing mushrooms at different stages of growth, captured both day and night, along with measurements of temperature, humidity, and air quality. This resource is designed to help scientists and farmers develop computer programs that can automatically identify mushrooms, determine if they’re ready to harvest, and predict growth patterns.

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Mushroom data creation, curation, and simulation to support classification tasks

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This study creates a new dataset of over 61,000 mushroom records from 173 species to help computers learn to identify whether mushrooms are safe to eat or poisonous. The researchers extracted mushroom information from an identification textbook and used computer programs to generate realistic hypothetical mushroom entries. They tested different AI methods and found that random forests (a type of machine learning algorithm) worked best, achieving perfect accuracy in identifying poisonous versus edible mushrooms.

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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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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.

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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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