YOLO – 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.

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.

Research Keyword: YOLO

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

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