Agronomists typically use specialized equipment in the laboratory to analyze leaf samples to accurately diagnose plant and tree health. Determining leaf water potential helps them estimate tree stress levels and optimize irrigation. While this has been mostly a manual process some research has been conducted on the use of drones and ground robots.
Autonomous collection of a leaf sample from a tree presents unique challenges in visual perception, actuation and leaf extraction. While computer vision techniques have made significant advances there are significant challenges which impact their real-time application:
Research team led by Prof. Konstantinos Karydis at UCR has developed a robotic system to autonomously select, cut and retrieve a leaf sample for water potential measurement and analysis. The developed system includes a visual perception algorithm and a new end-effector to cleanly cut leaves at their stem and retrieve them. The resulting robotic platform is a 6-degree of freedom robotic arm that automates the leaf retrieval process.
Leaf detection and extraction system utilizes a custom built end-effector attached to a mobile manipulator to cleanly cut leaves at their stems.
Working prototype of the robot has been built and demonstrated on live Avocado trees. The team is working towards removing the necessity of assuming a fixed angle of attack when approaching the leaf.
Please see all inventions by Prof. Karydis and his team at UCR.
Robotics, Precision Agriculture, Irrigation, Leaf Sampling, Leaf Water Potential, Tree Health, Plant Health, Tree Crops