Fruit Tree Cane Cut-Point Data for Sensor-Guided Pruning
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Solution Overview
Problem
Automating pruning operations in fruit trees, particularly in vineyards, is challenging due to the need for comprehensive judgment on health status, sun exposure, and ventilation, which varies among individual trees, making it difficult to determine optimal pruning points.
Innovation Solution
A method and system for generating cut-point data using sensors to group canes based on attributes like color, thickness, and bud distribution, 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 is implemented using sensors and algorithms, 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 pruning decision-making process is segmented into multiple independent sensor measurements (color, thickness, bud distribution, sun exposure, ventilation) that are collected and processed separately, then integrated to form a comprehensive pruning decision. This allows automated processing while maintaining judgment quality.
Solution Approach 2:
The sensor system is designed to perform multiple functions simultaneously - measuring cane attributes (color, thickness, bud distribution) and environmental conditions (sun exposure, ventilation) with a single integrated system, enabling comprehensive judgment through automated multi-parameter assessment.
2Manufacturing precision
If multiple sensor measurements and grouping operations are performed, then manufacturing precision of cut-point data is improved, but device complexity increases
Solution Approach 1:
The complex measurement and decision process is segmented into distinct operational phases: data collection from multiple sensors, grouping canes by attributes, evaluating grouped canes against pruning criteria, and generating cut-point data. This modular approach manages complexity while maintaining precision.
Solution Approach 2:
Canes are pre-grouped by similar attributes (color, thickness, bud distribution) before final pruning evaluation. This preliminary organization simplifies subsequent decision-making and reduces the computational complexity of evaluating each cane individually while maintaining accurate cut-point determination.
3Manufacturing precision
If individualized pruning decisions are made for each cane based on multiple attributes, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
Canes are pre-grouped by similar attributes (color, thickness, bud distribution, sun exposure, ventilation) before detailed evaluation. This preliminary classification allows the system to process canes more efficiently by evaluating groups rather than treating each cane completely independently, reducing total processing time while maintaining individualized precision through attribute-based grouping.
Solution Approach 2:
Multiple sensor measurements for each cane (color, thickness, bud distribution, sun exposure, ventilation) are merged and evaluated together in an integrated assessment. This combining of multiple attributes into a unified evaluation process improves pruning precision while avoiding the time loss of sequential separate evaluations.
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 (S220), determining one or more canes having been grouped into one of the plurality of groups each as a cane to be removed or a cane to be retained (S222), based on a distribution of buds on the cane(s) determined as a cane(s) to be retained for each of the plurality of groups, determining the plurality of canes each as a cane to be removed or a cane to be retained (S290), and generating the cut-point data for each cane determined to be removed (S300).