The Monash Apple Retrieving System: A review on system intelligence and apple harvesting performance

Abstract

The wide-scale adoption of selective apple harvesting robots is yet to be seen, due to their strict requirement of well-manicured canopies to perform reliably. However, maintaining this standard of canopy is labour and resource-intensive, and an unsustainable practice for farmers in the long term who are already burdened with labour shortages. Without a complete planner and intelligent decision-making, a robotic harvesting platform would struggle to adapt to, and navigate in even typical canopy environments. In this work, we introduce the Monash Apple Retrieving System (MARS), a selective apple harvesting platform that is capable of navigating and harvesting apples in complex canopy environments without the need for canopy simplification. Utilising a four-tier planning process, MARS is able to generate gentle, collision-free harvesting trajectories in complex and unstructured canopies. Extensive trials were conducted over the 2021 and 2022 apple harvesting seasons in Australia, where MARS achieved a harvesting success rate of 62.8% at a cycle time of 9.18 s per apple, with negligible damage to the fruit. Performance increased to 70.8% success rate and 7.91 s of cycle time under ideal visibility conditions. Although performance can be further enhanced with canopy simplification and hardware upgrades, the reported results were achieved under realistic canopy conditions where very limited attempts were made to modify the canopy to create idealistic conditions for robotic harvesting. This baseline performance is promising for farmers who are wanting to utilise robotic harvesting, but do not have the resources to maintain well-manicured canopies. By lowering strict canopy requirements for robotic harvesting, we can improve accessibility of this technology to more farmers who are struggling with labour shortages around the world.