# Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning

## TL;DR

This paper investigates using deep reinforcement learning to optimize camera viewpoints for autonomous strawberry harvesting, demonstrating significant improvements in decision-making efficiency in a simulated environment.

## Contribution

It introduces a novel deep reinforcement learning algorithm for viewpoint optimization in autonomous harvesting, addressing perception bottlenecks and showing promising simulation results.

## Key findings

- Agent achieves 8.7x higher returns than random actions
- Agent explores 8.8% faster than baseline visual servoing
- Agent successfully fixates on favorable viewpoints without explicit temporal information

## Abstract

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.