  Using robots to harvest sweet peppers in protected cropping environments has
remained unsolved despite considerable effort by the research community over
several decades. In this paper, we present the robotic harvester, Harvey,
designed for sweet peppers in protected cropping environments that achieved a
76.5% success rate (within a modified scenario) which improves upon our prior
work which achieved 58% and related sweet pepper harvesting work which achieved
33\%. This improvement was primarily achieved through the introduction of a
novel peduncle segmentation system using an efficient deep convolutional neural
network, in conjunction with 3D post-filtering to detect the critical cutting
location. We benchmark the peduncle segmentation against prior art
demonstrating a considerable improvement in performance with an F_1 score of
0.564 compared to 0.302. The robotic harvester uses a perception pipeline to
detect a target sweet pepper and an appropriate grasp and cutting pose used to
determine the trajectory of a multi-modal harvesting tool to grasp the sweet
pepper and cut it from the plant. A novel decoupling mechanism enables the
gripping and cutting operations to be performed independently. We perform an
in-depth analysis of the full robotic harvesting system to highlight
bottlenecks and failure points that future work could address.
