3D Cane Cut-Point Data for Sensor-Based Fruit Tree Pruning
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Solution Overview
Problem
Automating the pruning of fruit trees, particularly in orchards like vineyards, is challenging due to the need for comprehensive judgments on health status, sun exposure, and ventilation, which are difficult to replicate in an automated system.
Innovation Solution
A method and system for generating cut-point data using sensors to acquire three-dimensional cane attributes, determining priority levels, and generating data for a cutter's position to automate pruning by retaining or removing canes based on calculated scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated pruning system is implemented, then productivity is improved, but device complexity increases due to need for comprehensive judgment of health status, sun exposure, and ventilation
Solution Approach 1:
The pruning decision process is segmented into multiple independent evaluation dimensions (health status, sun exposure, ventilation, etc.), each assessed separately by dedicated sensors and algorithms. This allows the complex automated pruning system to break down the overall decision-making into manageable segments, improving productivity while controlling system complexity through modular architecture.
Solution Approach 2:
The system transforms qualitative pruning judgments into quantitative parameter measurements (e.g., converting health status assessment into measurable sensor data). By changing the parameters from subjective evaluations to objective measurable quantities, the system achieves automated high-speed pruning decisions without requiring complex human-like judgment capabilities.
2Manufacturing precision
If comprehensive judgment of health status, sun exposure, and ventilation is performed, then manufacturing precision is improved, but measurement precision requirements increase
Solution Approach 1:
The system evaluates cane attributes across multiple dimensions (health status, sun exposure, ventilation, position, orientation) rather than relying on single-dimensional measurements. By adding evaluation dimensions, the system achieves comprehensive pruning precision without requiring extremely high precision in any single measurement, as the overall decision is based on the synthesis of multiple dimensional assessments.
Solution Approach 2:
The system introduces intermediate evaluation metrics that bridge the gap between raw sensor measurements and final pruning decisions. These intermediate metrics (e.g., composite health scores, aggregated environmental assessments) serve as mediators that translate multiple imprecise measurements into a precise overall evaluation, enabling accurate pruning decisions without requiring each individual measurement to be highly precise.
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, for each of one or more canes of the fruit tree, acquiring measurement values concerning two or more attributes based on sensor data of the one or more canes being acquired by a sensor or sensors, acquiring information on priority levels of the two or more attributes, determining the one or more canes each as a cane to be removed or a cane to be retained based on the measurement values and the priority levels, and generating the cut-point data for each cane determined as a cane to be removed.