Robotic Leaf Extraction With Pose-Guided Stem Cutting
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Solution Overview
Problem
Current robotic systems for leaf sampling in agriculture are inefficient and labor-intensive, particularly for tree crops, as they often require manual collection and analysis of leaf water potential, which is critical for optimizing irrigation, and existing robotic methods struggle with precise leaf cutting and retention for further analysis.
Innovation Solution
A robotic system integrating a visual perception algorithm and a custom end-effector to autonomously detect, localize, and cleanly cut leaves at their stems, using a depth camera and a pneumatic system to align and retain the leaf for subsequent stem water potential analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual leaf collection is used, then leaf water potential measurements can be obtained, but significant human labor is required and labor costs increase
Solution Approach 1:
The robotic system performs self-service by autonomously navigating to target leaves, grasping them with the end-effector, and extracting leaf samples without requiring human intervention. The system independently completes the entire leaf collection process that previously required manual human labor, thereby resolving the contradiction between productivity and ease of operation.
Solution Approach 2:
The patent replaces the manual mechanical system with an automated robotic system. The robotic end-effector uses controlled mechanical forces to grasp and extract leaves, substituting human manual operations with programmable robotic mechanisms. This substitution maintains operational effectiveness while eliminating human labor requirements.
2Ease of operation
If robotic leaf sampling is implemented, then labor burden decreases, but precise leaf cutting and retention control becomes more difficult
Solution Approach 1:
The end-effector applies local quality by concentrating cutting force at a specific localized point on the leaf stem rather than applying force distributed over the entire leaf. The blade is positioned precisely at the stem location where cutting is needed, allowing clean separation of the leaf from the branch while maintaining control. This localized approach enables precise cutting despite the complexity of robotic manipulation.
Solution Approach 2:
The system performs preliminary action by first grasping the leaf securely with the end-effector before attempting to cut it. This preliminary grasping step ensures that the leaf is stable and properly positioned, which facilitates subsequent precise cutting. The sequential execution of grasping followed by cutting allows each action to be optimized independently, improving overall precision.
3Device complexity
If a single tree is used for measurements, then instrument operation is simplified, but measurement coverage and frequency are reduced
Solution Approach 1:
The robotic system provides multi-functionality by being able to access and sample leaves from multiple different trees throughout the orchard, not just a single tree. The mobile robotic platform can navigate between trees, and the end-effector can adapt its grasping and cutting actions to accommodate variations in leaf positions and orientations across different trees. This universal capability dramatically increases measurement coverage while maintaining operational simplicity through automation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and frequent leaf sampling with reduced human labor, improving the precision and frequency of irrigation management by automating the leaf water potential measurement process.
Implementation Method 1
a pneumatic system to align and retain the leaf
Data Source
AI summary
Disclosed herein is a system and methods for leaf detection and extraction. The extracted leaf may be used for leaf water potential analysis. In some embodiments, the method comprises identifying the leaf from a point cloud based on an image, determining a pose of the leaf based on the point cloud, and cutting and storing the leaf based on the pose of the leaf.