Research Keyword: object detection

The Application of an Intelligent Agaricus bisporus-Harvesting Device Based on FES-YOLOv5s

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Researchers developed an intelligent robot that automatically harvests button mushrooms with high precision. The system uses AI-powered camera vision to identify mature mushrooms in crowded growing beds, then carefully picks them with a gentle robotic arm. Testing showed the robot successfully harvests over 94% of mushrooms while causing minimal damage, making commercial mushroom farming more efficient and cost-effective.

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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.

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Improved Real-Time Detection Transformer with Low-Frequency Feature Integrator and Token Statistics Self-Attention for Automated Grading of Stropharia rugoso-annulata Mushroom

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This research presents an improved artificial intelligence system for automatically grading Stropharia rugoso-annulata (wine cap) mushrooms based on their size and quality. The new system uses advanced computer vision techniques to analyze mushroom images in real-time, achieving 95.2% accuracy while being efficient enough to run on smaller computing devices used in food processing facilities. By combining wavelet analysis for capturing overall mushroom shape with a streamlined attention mechanism, the system successfully grades mushrooms faster and more consistently than manual sorting, potentially reducing labor costs in industrial mushroom production.

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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.

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Detection and classification of Shiitake mushroom fruiting bodies based on Mamba YOLO

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Researchers developed an artificial intelligence system called Mamba-YOLO that can automatically detect and grade shiitake mushrooms for harvest. The system looks at images of mushrooms and identifies which ones are ready to pick based on their size, maturity, and surface texture characteristics. With 98.89% accuracy and fast processing speed of 8.3 milliseconds, this technology could help automate mushroom harvesting and reduce labor costs for farmers. The compact model design also allows it to be installed on robotic harvesting machines.

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