  Contemporary robots in precision agriculture focus primarily on automated
harvesting or remote sensing to monitor crop health. Comparatively less work
has been performed with respect to collecting physical leaf samples in the
field and retaining them for further analysis. Typically, orchard growers
manually collect sample leaves and utilize them for stem water potential
measurements to analyze tree health and determine irrigation routines. While
this technique benefits orchard management, the process of collecting,
assessing, and interpreting measurements requires significant human labor and
often leads to infrequent sampling. Automated sampling can provide highly
accurate and timely information to growers. The first step in such automated
in-situ leaf analysis is identifying and cutting a leaf from a tree. This
retrieval process requires new methods for actuation and perception. We present
a technique for detecting and localizing candidate leaves using point cloud
data from a depth camera. This technique is tested on both indoor and outdoor
point clouds from avocado trees. We then use a custom-built leaf-cutting
end-effector on a 6-DOF robotic arm to test the proposed detection and
localization technique by cutting leaves from an avocado tree. Experimental
testing with a real avocado tree demonstrates our proposed approach can enable
our mobile manipulator and custom end-effector system to successfully detect,
localize, and cut leaves.
