Fruit Tree Cane Grouping for Automated 3D Pruning
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Solution Overview
Problem
Automating pruning work for fruit trees, particularly in vineyards, is challenging due to the need for comprehensive judgments on health status, sun exposure, and ventilation, which are difficult to replicate in unmanned systems.
Innovation Solution
A method and system for generating cut-point data using sensors to group canes based on attributes like color, thickness, and direction, determining which canes to remove or retain, and controlling a cutter's three-dimensional position for precise pruning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated pruning systems are implemented, then productivity and consistency are improved, but the ability to make comprehensive judgments on health status, sun exposure, and ventilation deteriorates
Solution Approach 1:
The system segments the complex pruning decision into multiple independent sensor measurements (color for health status, light sensors for sun exposure, spatial sensors for ventilation). Each sensor captures a specific aspect of cane quality, and the control unit integrates these segmented data points to make the final pruning decision, resolving the contradiction by making automation as comprehensive as human judgment through multi-dimensional data collection
Solution Approach 2:
The control unit acts as an intermediary between the physical cane properties and the pruning decision. It receives data from multiple sensors (color sensors, light sensors, spatial sensors), processes this information, and translates it into automated pruning commands. This intermediary layer enables automated systems to make reliable judgments by synthesizing multiple measurement dimensions
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 control unit is designed as a universal processing device that handles multiple types of sensor inputs (color data, light intensity data, spatial position data) through a single integrated system. This multi-functional controller reduces overall system complexity by consolidating multiple processing functions into one unit, while still achieving high pruning decision accuracy through comprehensive attribute measurement
Solution Approach 2:
The system replaces complex mechanical human judgment processes with electronic sensor measurements and digital data processing. Instead of relying on human senses and experience, the system uses electronic sensors to measure color, light exposure, and spatial characteristics, then processes this data electronically to make pruning decisions, achieving high precision while managing complexity through electronic rather than mechanical means
Data Source
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AI summary
The invention concerns 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 (S220), 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 (S222), and generating the cut-point data for each cane determined as a cane to be removed (S300).