Research Topic: automated harvesting

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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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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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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