Sensor-Guided Cut-Point Data for Automated Cane 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 that includes grouping canes based on sensor data, determining which canes to remove or retain, and controlling a cutter's three-dimensional position using generated data to perform pruning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual pruning is performed by human workers, then comprehensive judgment on health status, sun exposure, and ventilation can be made, but labor cost and time consumption increase
Solution Approach 1:
The patent replaces manual mechanical pruning operations with an automated system comprising sensors (optical, LiDAR, ultrasonic), data processing units, and robotic actuators. The system captures multi-dimensional data about canes and their environment, processes this information through algorithms, and executes pruning actions automatically, substituting human mechanical judgment and action with automated sensing and actuation systems.
Solution Approach 2:
The patent creates a digital replica or model of the physical pruning decision-making process. Sensors capture data about cane health, position, and environment, which is then processed to generate a digital representation of the optimal pruning strategy. This digital model guides the physical pruning execution, allowing comprehensive analysis without human time constraints.
2Productivity
If automated pruning systems are implemented, then productivity increases, but system complexity and initial cost increase
Solution Approach 1:
The patent divides the automated pruning system into distinct functional modules: sensing subsystems (optical sensors, LiDAR, ultrasonic sensors), data processing subsystems (controllers, algorithms), and actuation subsystems (robotic arms, cutters). Each module performs a specific function and can be independently optimized or replaced, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The patent designs the automated pruning system with multi-functional components that can perform multiple tasks. For example, the sensor array can detect cane position, health status, and environmental conditions simultaneously; the control system can process various types of sensor data and generate appropriate pruning commands. This universality reduces the number of separate components needed, simplifying the overall system.
3Measurement precision
If multiple sensor types are used for comprehensive cane assessment, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (optical sensors for color and texture, LiDAR for three-dimensional positioning, ultrasonic sensors for distance measurement) into an integrated sensing array. These sensors work simultaneously and their data is processed together by a unified control system, creating a comprehensive view of cane attributes without requiring separate independent systems for each measurement type.
Solution Approach 2:
The patent introduces a data processing intermediary layer that receives raw data from multiple sensor types, standardizes and integrates this information, and presents it to the control algorithm in a unified format. This intermediary processing layer simplifies the complexity of handling multiple sensor types by providing a consistent data interface, allowing precise measurement without proportionally increasing system complexity.
Data Source
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AI summary
The invention concerns a method for generating cut-point data including information indicating a three-dimensional position of a point on a cane of a fruit tree (200) where the cane is to be cut off, includes, for each of one or more canes of the fruit tree, acquiring (S100) a measurement value(s) concerning one or more attributes including an attribute concerning vigor of the fruit tree (200), based on sensor data of the one or more canes, determining (S200) the one or more canes each as a cane to be removed or a cane to be retained based on the measurement value(s), determining (S280) a number of buds to be retained on each cane determined as a cane to be retained, generating the cut-point data for each cane determined as a cane to be removed, and based on the number of buds to be retained, generating (S302) the cut-point data for each cane determined as a cane to be retained.