Development of a Robotic Harvesting system for Sustainable Button Mushroom Production

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.