Automated Implement Control System with Dynamic Distance Sensor Confidence Switching
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Solution Overview
Problem
Agricultural implement control systems face challenges in accurately positioning sprayer booms due to inconsistent and unreliable distance measurements from sensors, particularly when measuring crop height or navigating uneven terrain, leading to potential collisions or inefficient application of agricultural products.
Innovation Solution
An automated implement control system that uses multiple distance sensors to measure both ground and canopy distances, determines confidence values based on rate of change comparisons, and selects the most reliable distance as a control basis to guide the implement, incorporating a kinematic model for predictive window generation and switching between control bases as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensor measurements are used to control implement position, then automation is improved, but measurement reliability deteriorates due to inconsistent ground/crop detection
Solution Approach 1:
The system dynamically switches between ground-based control and canopy-based control modes based on real-time sensor confidence assessments. When ground measurements become unreliable (e.g., obscured by crop), the system transitions to using canopy distance measurements, and vice versa. This dynamic adaptation maintains automated control reliability despite varying field conditions.
Solution Approach 2:
The system changes the control parameter basis from ground distance to canopy distance (or vice versa) depending on which measurement type has higher confidence. This parameter switching allows the control system to maintain accuracy by using the most reliable measurement type available under current conditions.
2Measurement precision
If multiple sensor measurements are processed to improve reliability, then measurement precision is improved, but system complexity increases
Solution Approach 1:
The system uses feedback from confidence assessments of multiple sensor measurements to determine which measurement basis (ground or canopy) to trust. The confidence level of each measurement type is continuously evaluated, and the system adjusts its control basis accordingly, creating a closed-loop system that improves precision through intelligent selection rather than complex processing of all measurements.
3Speed
If the implement follows unreliable sensor measurements, then responsiveness is improved, but collision risk increases
Solution Approach 1:
The confidence assessment mechanism acts as an intermediary between raw sensor measurements and implement control actions. Instead of directly responding to all sensor inputs, the system uses confidence levels as a filter to determine which measurements are trustworthy enough to trigger implement movements, thereby preventing collisions caused by erroneous measurements while maintaining responsive control.
Data Source
AI summary
An automated implement control system includes one or more distance sensors configured for coupling with an agricultural implement. The one or more distance sensors are configured to measure a ground distance and a canopy distance from the one or more sensors to the ground and crop canopy, respectively. An implement control module is in communication with the one or more distance sensors. The implement control module controls movement of the agricultural implement. The implement control module includes a confidence module configured to determine a ground confidence value based on the measured ground distance and a canopy confidence value based on the measured canopy distance. A target selection module of the implement control module is configured to select one of the measured ground or canopy distances as a control basis for controlling movement of the agricultural implement based on the comparison of confidence values.