Evaluation and Analysis of Precision Leaf Pruning End-Effectors Within Dense Foliage Agriculture

Abstract

Physical interaction tasks within dense foliage such as leaf pruning and fruit harvesting are current challenges for agricultural robotics. This is due to the cluttered and unstructured environment these tasks are conducted within, complex stem structures providing a number of obstacles that obscure and constrain end-effector operational workspaces. Therefore, enabling robots to operate within a dense foliage environment requires a purpose-built end-effector, able to perform precision based tasks despite workspace challenges. Whilst many tools have been implemented within related literature, most prior work evaluates their operational performance within reduced foliage testing environments. As such, this paper presents the performance evaluation of three end-effector mechanisms developed for robotic leaf pruning operations within unaltered dense foliage, referred to as the: scissor-cutter, curved-cutter and vacuum-cutter. End-effector mechanisms were chosen based on compact tool shapes, target approach direction range and deployment within related literature. Evaluation criteria focused on the mechanisms operational success rate and damage caused to the plant. Through this paper we show that a vacuum-cutter had the highest success rate of 75% whilst the scissor-cutter caused the least plant damage. A comprehensive failure mechanism assessment and improvement recommendations for future prototypes are also provided.