Robotic Assessment of a Crop's Need for Watering

This paper focuses on developing a robot-assisted system for stem water potential (SWP) measurement in or- chards. SWP is a metric frequently used by agronomists and growers to optimize irrigation schedules for crops. However, such measurements are currently being made via a time- and labor-intensive procedure that faces the challenges of sparse sampling and human variability in determining SWP. In response to these challenges, our proposed robotic system aims to automate time-consuming and difficult to perform tasks, by collecting multiple leaves and automating some parts of the overall SWP analysis process. To achieve so, this work considers three core components: 1) informed planning, to determine where to collect leaves to get the most informative readings; 2) system design and integration for autonomous leaf retrieval with a mobile manipulator and a custom-made end-effector; and 3) learning-based machine vision for automated visual identification of leaf xylem wetness during SWP analysis. Taken together, these constitute the core building-blocks toward en- abling complete robot autonomy in physical specimen sampling and transport in the field.