  Robotic harvesting has the potential to positively impact agricultural
productivity, reduce costs, improve food quality, enhance sustainability, and
to address labor shortage. In the rapidly advancing field of agricultural
robotics, the necessity of training robots in a virtual environment has become
essential. Generating training data to automatize the underlying computer
vision tasks such as image segmentation, object detection and classification,
also heavily relies on such virtual environments as synthetic data is often
required to overcome the shortage and lack of variety of real data sets.
However, physics engines commonly employed within the robotics community, such
as ODE, Simbody, Bullet, and DART, primarily support motion and collision
interaction of rigid bodies. This inherent limitation hinders experimentation
and progress in handling non-rigid objects such as plants and crops. In this
contribution, we present a plugin for the Gazebo simulation platform based on
Cosserat rods to model plant motion. It enables the simulation of plants and
their interaction with the environment. We demonstrate that, using our plugin,
users can conduct harvesting simulations in Gazebo by simulating a robotic arm
picking fruits and achieve results comparable to real-world experiments.
