MushR: A Smart, Automated, and Scalable Indoor Harvesting System for Gourmet Mushrooms

Abstract

Gourmet mushrooms are foraged from the wild or grown indoors in controlled environments. Indoor mushroom farms with controlled growth environments allow for all-year-round growing. However, it remains a labor-intensive process. We propose MushR as a modular and scalable gourmet mushroom growing and harvesting system that goes beyond the state of the art, which merely monitors and controls the growing environment, by introducing an image recognition system that determines when and which mushrooms are ready to be harvested in conjunction with a proof of concept of an automated mushroom harvesting mechanism for harvesting the mushrooms without human interaction. The image recognition setup monitors the growing status of the mushrooms and guides the harvesting process. We present a Mask R-CNN model for the detection of oyster mushroom maturity with a 91.7% training accuracy and a semiautomated harvesting system, integrating a Raspberry Pi for control, an electrical switch, an air compressor, and a pneumatic cylinder with a cutting knife to facilitate timely mushroom harvesting. The modularity and scalability of the system allow for industry-level usage and can be scaled according to the required mushroom-growing systems within the facility. The AI model, its underlying dataset, a digital twin for mushroom production, the setup of our growth and control chambers, and additional information are all made available under an open-source license.

Keywords

• gourmet mushroom
• digital twin
• AI
• Mask R-CNN
• IoT
• automation