Robotic Vine Tying Using AI Grab-Point Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for tying or twisting agricultural items like grape vine canes to support structures such as wire trellises are manual, time-consuming, and inconsistent, leading to unreliability in securing and fastening, which affects vine health and grape quality.
Innovation Solution
An agricultural tool and method using AI image segmentation and depth estimation to identify and generate grab-points and tie-points for automated attachment of agricultural items to support structures, employing a robotic system for precise and reliable fastening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual tying or twisting methods are used to secure agricultural items to support structures, then the process can be performed with simple tools, but the task becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical operations with an automated robotic system that uses computer vision (AI image segmentation) to identify grab points and a robotic manipulator to execute the tying/twisting action. This substitution of manual mechanical systems with automated mechanical systems resolves the contradiction by dramatically increasing productivity while maintaining operational capability through sophisticated sensing and control.
Solution Approach 2:
The system enables the agricultural item to be secured autonomously through self-service automation. The robotic system independently performs the entire sequence of identifying the agricultural item, determining grab points, positioning the tool, and executing the tying/twisting action without human intervention, thereby resolving the productivity limitation of manual methods.
2Ease of operation
If manual tying or twisting methods are used, then the process can be performed with basic skills, but the consistency and reliability of securing varies from person to person
Solution Approach 1:
The patent replaces variable human manual operations with a consistent automated robotic system. The AI image segmentation algorithm uniformly identifies grab points, and the robotic manipulator executes identical tying/twisting motions for each agricultural item, eliminating the variability inherent in manual operations performed by different people. This resolves the contradiction by maintaining ease of operation through automation while achieving high reliability and consistency.
Solution Approach 2:
The system incorporates feedback through AI image segmentation that continuously identifies and verifies grab points before and during the tying/twisting process. This visual feedback mechanism ensures consistent and reliable execution by allowing the system to detect and correct any deviations from the intended operation, thereby resolving the reliability inconsistency problem of manual methods.
3Productivity
If automated robotic systems are used to secure agricultural items, then productivity and consistency improve, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex automated system into distinct functional modules: an AI image segmentation module for identifying grab points, a robotic manipulator for positioning and executing the tying/twisting action, and a control system for coordinating operations. This modular segmentation manages device complexity by making each component independently developable, testable, and replaceable, thereby enabling high productivity while controlling overall system complexity.
Solution Approach 2:
The robotic system is designed with universality to perform multiple agricultural securing operations (tying, twisting, fastening) using a single integrated platform. The AI image segmentation algorithm can identify various agricultural items and support structures, and the robotic manipulator can execute different securing actions, thereby achieving high productivity across multiple tasks without proportionally increasing device complexity.
4Reliability
If automated robotic systems with AI image segmentation are used, then reliability and consistency of securing improve, but the device complexity and cost increase
Solution Approach 1:
The patent replaces variable human manual operations with a consistent automated robotic system enhanced by AI image segmentation. This substitution achieves high reliability and consistency in securing agricultural items by eliminating human variability, while the AI component provides intelligent grab point identification that adapts to different agricultural items and conditions, managing the complexity through software-based intelligence rather than purely mechanical complexity.
Solution Approach 2:
The system achieves reliability through parameter changes in the AI image segmentation algorithm, which adjusts its detection and identification parameters to accurately locate grab points on various agricultural items and support structures. This software-based parameter adjustment provides adaptability and consistency without requiring complex mechanical adjustments, thereby improving reliability while controlling device complexity.
Data Source
AI summary
A method includes generating an image, segmenting the image to identify a component of an agricultural item, detecting one or more agricultural features of the agricultural item based on the image, the one or more agricultural features being associated with the component of the agricultural item, generating a two-dimensional grab-point based on the component of the agricultural item and the one or more agricultural features, and generating a three-dimensional grab-point based on the two-dimensional grab-point and a depth estimation of the agricultural item.