Research Keyword: mushroom 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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Design and test analysis of a rotary cutter device for root cutting of golden needle mushroom

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Scientists developed a new high-speed rotary cutting machine specifically designed for harvesting golden needle mushrooms efficiently and gently. The machine uses a specially angled blade that slides through the delicate mushroom stems rather than crushing them, resulting in cleaner cuts and less damage. Testing showed this new design cuts the required cutting force in half and uses significantly less energy compared to existing methods.

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Design and test analysis of a rotary cutter device for root cutting of golden needle mushroom

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This study develops a specialized high-speed cutting machine for harvesting golden needle mushrooms, which are currently picked by hand due to their delicate nature. The machine uses a fast-spinning blade with a special sliding angle that cuts smoothly rather than crushing the tender stems, similar to how a sharp knife slides through vegetables rather than crushing them. Testing showed this approach reduces energy use and damage while improving cutting quality, making mushroom harvesting more practical for commercial scale operations.

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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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