Crop design for improved robotic harvesting: A case study of sweet pepper harvesting

Abstract

Current harvesting robots have limited performance, due to the unstructured and dynamic nature of both the target crops and their environment. Efforts to date focus on improving sensing and robotic systems. This paper presents a parallel approach, to ‘design’ the crop and its environment to best fit the robot, similar to robotic integration in industrial robot deployments. A systematic methodology to select and modify the crop ‘design’ (crop and environment) to improve robotic harvesting is presented. We define crop-dependent robotic features for successful harvesting (e.g., visibility, reachability), from which associated crop features are identified (e.g., crop density, internode length). Methods to influence the crop features are derived (e.g., cultivation practices, climate control) along with a methodological approach to evaluate the proposed designs. A case study of crop ‘design’ for robotic sweet pepper harvesting is presented, with statistical analyses of influential parameters. Since comparison of the multitude of existing crops and possible modifications is impossible due to complexity and time limitations, a sequential field experimental setup is planned. Experiments over three years, 10 cultivars, two climate control conditions, two cultivation techniques and two artificial illumination types were performed. Results showed how modifying the crop effects the crops characteristics influencing robotic harvesting by increased visibility and reachability. The systematic crop ‘design’ approach also led to robot design recommendations. The presented ‘engineering’ the crop ‘design’ framework highlights the importance of close synergy between crop and robot design achieved by strong collaboration between robotic and agronomy experts resulting in improved robotic harvesting performance.