3D Cane Cut-Point Data for Autonomous Fruit Tree Pruning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automating pruning work for fruit trees, particularly in vineyards, is challenging due to the need for comprehensive judgment of health status, sun exposure, and ventilation, which is difficult to replicate in unmanned systems.
Innovation Solution
A method and system for generating cut-point data using sensors to determine the three-dimensional position of cane cuts, grouping canes based on sensor data, and using a controller to guide a cutter for precise pruning, considering attributes like color, direction, thickness, and bud direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated pruning systems are implemented, then productivity and efficiency are improved, but the ability to make comprehensive judgments about health status, sun exposure, and ventilation deteriorates
Solution Approach 1:
The patent replaces human mechanical judgment with sensor-based detection systems. Multiple sensors (cameras, LIDAR, environmental sensors) automatically detect and evaluate cane attributes such as position, orientation, thickness, and health indicators, substituting the need for human expert assessment while maintaining objective decision-making criteria for pruning operations
Solution Approach 2:
The system creates digital representations (3D point clouds, attribute data models) of the physical cane structure and environment. These digital copies are then analyzed by processing units that apply pruning decision logic, effectively copying the human judgment process into a computational framework that can be executed by autonomous machinery
2Manufacturing precision
If multiple attributes are measured for each cane, then manufacturing precision of pruning decisions is improved, but device complexity increases
Solution Approach 1:
The patent segments the pruning decision-making process into distinct functional modules: sensor acquisition units for collecting raw data, attribute extraction units for processing specific cane characteristics (position, orientation, thickness, health), evaluation units for assessing pruning criteria, and control units for executing cutting commands. This segmentation allows each module to specialize in specific measurements while maintaining overall system manageability
Solution Approach 2:
The system employs multi-functional sensors and processing units that can detect multiple attributes simultaneously. For example, a single imaging system captures both spatial position and visual health indicators, while the processing unit evaluates multiple pruning criteria (sun exposure, ventilation, structural integrity) using the same data set, reducing the need for separate dedicated devices for each measurement
Data Source
AI summary
A method for using a computer or computers to generate cut-point data including information indicating a three-dimensional position of a point on a cane of a fruit tree where the cane is to be cut off, includes grouping a plurality of canes of the fruit tree into a plurality of groups based on sensor data of the plurality of canes acquired by a sensor or sensors, determining one or more canes grouped into a same group among the plurality of groups each as a cane to be removed or a cane to be retained, and generating the cut-point data for each cane determined as a cane to be removed.