  Rising global food demand and harsh working conditions make fruit harvest an
important domain to automate. Peduncle localization is an important step for
any automated fruit harvesting system, since fruit separation techniques are
highly sensitive to peduncle location. Most work on peduncle localization has
focused on computer vision, but peduncles can be difficult to visually access
due to the cluttered nature of agricultural environments. Our work proposes an
alternative method which relies on mechanical -- rather than visual --
perception to localize the peduncle. To estimate the location of this important
plant feature, we fit wrench measurements from a wrist force/torque sensor to a
physical model of the fruit-plant system, treating the fruit's attachment point
as a parameter to be tuned. This method is performed inline as part of the
fruit picking procedure. Using our orchard proxy for evaluation, we demonstrate
that the technique is able to localize the peduncle within a median distance of
3.8 cm and median orientation error of 16.8 degrees.
