Fruit Tree Cane Pruning Automation for Accurate Retention Decisions
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Solution Overview
Problem
Automating pruning work for fruit trees, particularly during dormancy, 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 using sensors and data processors to generate cut-point data for pruning fruit trees, determining which canes to remove or retain based on sensor data, and controlling a cutter to perform pruning operations.
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 on health status, sun exposure, and ventilation deteriorates
Solution Approach 1:
The patent replaces human expert judgment with an automated system using imaging devices (cameras, LiDAR), sensors, and machine learning algorithms to detect and evaluate cane characteristics, tree health status, sun exposure, and ventilation conditions, thereby substituting mechanical human decision-making with automated technological systems
Solution Approach 2:
The patent introduces multiple sensors and imaging devices as intermediaries between the pruning system and the fruit tree, capturing data on cane position, diameter, orientation, tree health, environmental conditions, and using machine learning models as intermediaries to process this data and generate pruning decisions
2Reliability
If manual pruning is performed by experts, then pruning quality and fruit yield are maintained, but labor intensity and time consumption increase
Solution Approach 1:
The system enables the pruning operation to serve itself by automatically detecting canes, evaluating their retention or removal status based on pre-set criteria and machine learning models, calculating optimal cut points, and controlling the cutter without continuous human intervention, allowing the system to autonomously complete the pruning task
Solution Approach 2:
The patent performs preliminary detection and evaluation of all canes, tree health status, sun exposure, and ventilation conditions before making pruning decisions, using machine learning models to pre-assess which canes should be retained or removed and where the optimal cut points are, before the actual cutting operation begins
Data Source
AI summary
A method includes using a computer or computers to receive sensor data of one or more canes of a fruit tree, the sensor data being acquired by a sensor or sensors, and determining the one or more canes each as a cane to be removed or a cane to be retained based on the sensor data.