  Robotic harvesting of crops has the potential to disrupt current agricultural
practices. A key element to enabling robotic harvesting is to safely remove the
crop from the plant which often involves locating and cutting the peduncle, the
part of the crop that attaches it to the main stem of the plant.
  In this paper we present a comparative study of two methods for performing
peduncle detection. The first method is based on classic colour and geometric
features obtained from the scene with a support vector machine classifier,
referred to as PFH-SVM. The second method is an efficient deep neural network
approach, MiniInception, that is able to be deployed on a robotic platform. In
both cases we employ a secondary filtering process that enforces reasonable
assumptions about the crop structure, such as the proximity of the peduncle to
the crop. Our tests are conducted on Harvey, a sweet pepper harvesting robot,
and is evaluated in a greenhouse using two varieties of sweet pepper, Ducati
and Mercuno. We demonstrate that the MiniInception method achieves impressive
accuracy and considerably outperforms the PFH-SVM approach achieving an F1
score of 0.564 and 0.302 respectively.
