Real-Time Sensor Bias Correction for Agricultural Machines
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Solution Overview
Problem
Current calibration methods for agricultural machines are time-consuming, prone to data inaccuracies, and fail to automatically correct post-calibration deficiencies, leading to calibration-induced offsets between machines and within a single machine, which affects the accuracy of agronomic parameter measurements.
Innovation Solution
A system that calibrates sensors in near real-time by aggregating and processing data from multiple machines, applying bias correction algorithms to reduce sensing system biases, and automatically adjusting sensor outputs to ensure accurate agronomic parameter measurements across multiple machines and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used for agricultural machines, then calibration can be performed, but it is time-consuming and prone to data inaccuracies
Solution Approach 1:
The system performs automatic calibration using sensor data from the agricultural machine itself and other machines in the fleet, eliminating the need for manual operator intervention. The calibration process is self-executing through automated algorithms that compare sensor readings against reference data and adjust calibration parameters accordingly.
Solution Approach 2:
The system continuously monitors sensor data and compares it against expected values or reference measurements from other machines. When discrepancies are detected, the system automatically adjusts calibration parameters and verifies the improvement, creating a closed-loop feedback system that iteratively optimizes measurement accuracy.
2Measurement precision
If traditional calibration methods are used, then calibration can be performed, but calibration-induced offsets occur between machines and within a single machine
Solution Approach 1:
The system combines sensor data from multiple agricultural machines to establish a fleet-wide calibration baseline. By aggregating data across machines and comparing individual machine readings against the collective dataset, the system identifies and corrects calibration offsets, ensuring consistency across the entire fleet while maintaining individual machine accuracy.
3Measurement precision
If manual calibration is performed, then calibration can be completed, but it fails to automatically correct post-calibration deficiencies
Solution Approach 1:
The system continuously monitors sensor data after calibration and automatically detects when measurements deviate from expected ranges. When post-calibration deficiencies are identified, the system automatically adjusts calibration parameters without requiring manual intervention, creating a self-correcting system that maintains accuracy over time.
Solution Approach 2:
Instead of performing calibration as a discrete manual task, the system continuously executes calibration adjustments based on real-time sensor data analysis. The calibration process is ongoing and adaptive, automatically correcting deficiencies as they arise rather than relying on periodic manual recalibration.
Data Source
AI summary
Sensor data, and the sensors themselves are calibrated, in near real time. Sensor data from multiple mobile machines is received on a mobile machine and used to calibrate sensor data on the mobile machine.