Research Keyword: Image Segmentation

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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Micro-CT and deep learning: Modern techniques and applications in insect morphology and neuroscience

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Modern scanning technology called micro-CT can create detailed 3D pictures of tiny insects and their brains without damaging them. Artificial intelligence using deep learning can automatically analyze these massive image files much faster than humans could. Scientists are combining these two technologies to map insect brains and sensory systems in unprecedented detail, potentially revealing how insects sense and process information from their environment.

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Research on an Improved Segmentation Recognition Algorithm of Overlapping Agaricus bisporus

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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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