Development of a Robotic Harvesting system for Sustainable Button Mushroom Production

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Commodities

• Miscellaneous: mushrooms

Practices

• Crop Production: food product quality/safety
• Farm Business Management: labor/employment

Agaricus bisporus, commonly known as button mushroom, should be harvested selectively by hand to preserve its high quality and delivered to the fresh market owing to its delicate skin, prone to bruise and drop in quality. However, the mushroom industry has confronted high costs and a shortage of skilled workers due to the labor-intensive environment of picking mushrooms. Studies have shown that harvesting mushrooms by bending and twisting individual mushrooms is effective in picking motions, as is the human harvesting approach. In the meantime, the pose and vicinity of each mushroom play crucial roles in picking them. This study aims to develop an autonomous robot to pick mushrooms using a vacuum cup-type end-effector with bending and twisting motion. Meanwhile, a machine vision system with a stereo camera will be developed for mushroom detection and decision-making on individual mushroom picking. Finally, a harvesting robot will be tested and evaluated by integrating the picking end-effector and the machine vision system. The outreach strategy includes the field demonstration of the developed robot to Pennsylvania growers and carrying out surveys, workshops, webinars, etc., to evaluate the current satisfaction and knowledge of the growers and educate them regarding the benefits of applying developed technology. The target of this proposal is to alleviate the labor shortage issue, which imposes high costs on the mushroom industry, and increase the quality and productivity of mushrooms.

The primary goal of this project is to develop a robotic harvesting system for picking button mushrooms in greenhouses. This research project will be focused on three objectives listed below:

1. Develop image processing and deep learning algorithms using YOLOv CNN methods to identify the location, maturity, and estimate of mushrooms’ pose.

The coupled image processing and deep learning algorithms provide a robust system to detect and segment mushrooms from their background in real-time. Of different deep learning algorithms, we will be using YOLOv convolutional neural networks to accomplish this objective accurately. In the meantime, the system could find the individual mushroom’s pose in 2D and 3D, maturity, center point, and circle. This information will be sent to the decision-making section to determine the next step.

2. Develop a decision-making algorithm to assign the picking sequence and the bending direction of mushrooms for the picking process.

By acquiring information from the last stage to carry on the decision-making process, our developed algorithm will be able to identify the sequence of picking mushrooms using image processing techniques such as morphology characteristics. In addition, as the targeted mushrooms will be picked by bending and twisting, and because they grow in clusters, we will identify the bending direction so as not to injure nearby mushrooms.

3. Develop an electrical and control system to move the robot's manipulator and a soft end-effector and pick mushrooms by bending and twisting mechanisms.

The collected information from the last objectives is fed to the control system to set the robot joints' rotation degrees, approaching orientation, plan to pick mushrooms, etc. As a result, the manipulation occurs, and a vacuum cup picks each mushroom, transfers it to the stem trimmer, and puts it in the specified location.