  This paper presents a 3D visual detection method for the challenging task of
detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting
the peduncle cleanly is one of the most difficult stages of the harvesting
process, where the peduncle is the part of the crop that attaches it to the
main stem of the plant. Accurate peduncle detection in 3D space is therefore a
vital step in reliable autonomous harvesting of sweet peppers, as this can lead
to precise cutting while avoiding damage to the surrounding plant. This paper
makes use of both colour and geometry information acquired from an RGB-D sensor
and utilises a supervised-learning approach for the peduncle detection task.
The performance of the proposed method is demonstrated and evaluated using
qualitative and quantitative results (the Area-Under-the-Curve (AUC) of the
detection precision-recall curve). We are able to achieve an AUC of 0.71 for
peduncle detection on field-grown sweet peppers. We release a set of manually
annotated 3D sweet pepper and peduncle images to assist the research community
in performing further research on this topic.
