Research Keyword: automated detection

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

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.

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Research on an Improved Segmentation Recognition Algorithm of Overlapping Agaricus bisporus

Scientists developed a new computer vision system that can automatically identify and locate overlapping button mushrooms (Agaricus bisporus) in factory farms. The system uses image processing techniques to overcome challenges like uneven lighting and crowded mushrooms. It successfully identified mushrooms with over 96% accuracy, which could help automate the mushroom harvesting process and reduce labor costs for farmers.

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