computer vision – mycolab.ai

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.

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.

Deep learning application to hyphae and spores identification in fungal fluorescence images

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Researchers developed an artificial intelligence system using two deep learning models to automatically identify fungal infections in microscope images. The system analyzes fluorescence-stained samples to detect fungal spores, hyphae, and mycelium with accuracy matching experienced doctors. This automated approach can significantly reduce the time clinicians spend examining samples and help prevent misdiagnosis, especially in hospitals with fewer specialist technicians.

Automatic classification of fungal-fungal interactions using deep learning models

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Researchers developed an artificial intelligence system that automatically analyzes images of fungi growing together to identify which ones can fight off disease-causing fungi. Instead of having humans manually look at thousands of plate images, which is time-consuming and subjective, their computer vision system can classify the outcomes with 95% accuracy. This automation tool could help scientists quickly find beneficial fungi that could replace chemical pesticides in agriculture, supporting the goal of sustainable and more environmentally friendly farming.

Deep learning application to hyphae and spores identification in fungal fluorescence images

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Researchers developed an artificial intelligence system that can automatically identify fungal infections in microscope images as accurately as experienced doctors. The system uses two different AI models working together to spot fungal spores, thread-like hyphae, and mycelium in fluorescence images. This technology could significantly reduce the time doctors spend analyzing samples and help ensure more accurate diagnoses, especially in hospitals with fewer experienced specialists.

Research Keyword: computer vision

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

Automatic classification of fungal-fungal interactions using deep learning models

Deep learning application to hyphae and spores identification in fungal fluorescence images

Automatic classification of fungal-fungal interactions using deep learning models

Deep learning application to hyphae and spores identification in fungal fluorescence images