An Integrated Actuation-Perception Framework for Robotic Leaf Retrieval: Detection, Localization, and Cutting

TL;DR

This paper introduces an integrated robotic system that detects, localizes, and cuts leaves in orchards, enabling automated in-situ leaf sampling for precision agriculture, reducing manual labor and increasing sampling frequency.

Contribution

It presents a novel perception-actuation framework combining point cloud-based leaf detection with a custom robotic end-effector for automated leaf harvesting.

Findings

Successful detection and localization of leaves in real orchard conditions

Effective cutting of leaves using a custom robotic end-effector

Demonstrated system operates reliably on avocado trees

Abstract

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.