  Autonomous harvesting may provide a viable solution to mounting labor
pressures in the United States's strawberry industry. However, due to
bottlenecks in machine perception and economic viability, a profitable and
commercially adopted strawberry harvesting system remains elusive. In this
research, we explore the feasibility of using deep reinforcement learning to
overcome these bottlenecks and develop a practical algorithm to address the
sub-objective of viewpoint optimization, or the development of a control policy
to direct a camera to favorable vantage points for autonomous harvesting. We
evaluate the algorithm's performance in a custom, open-source simulated
environment and observe encouraging results. Our trained agent yields 8.7 times
higher returns than random actions and 8.8 percent faster exploration than our
best baseline policy, which uses visual servoing. Visual investigation shows
the agent is able to fixate on favorable viewpoints, despite having no explicit
means to propagate information through time. Overall, we conclude that deep
reinforcement learning is a promising area of research to advance the state of
the art in autonomous strawberry harvesting.
