Yield Sensor Calibration Using Consistent Field Zones
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Solution Overview
Problem
Crop harvesting machines face challenges in ensuring accurate yield measurements from sectional and aggregate yield sensors due to wear and varying conditions in the field, necessitating effective calibration methods to maintain precision.
Innovation Solution
The system identifies candidate calibration zones in the field with consistent topography and yield using geolocation and sensor data, allowing for in-situ calibration of yield sensors during operation, ensuring accurate yield measurements by comparing estimated and measured yields to calculate sensor-specific errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If yield sensors are used to detect and report sectional and aggregate yield information, then yield measurement capability is provided, but measurement precision deteriorates due to sensor wear and varying field conditions
Solution Approach 1:
The system performs preliminary identification of calibration zones using historical field data, topographical information, and yield models before actual calibration is needed. This allows the calibration process to be prepared in advance, ensuring that when calibration occurs, the system can quickly and accurately determine the appropriate calibration parameters without delay or reduced precision.
Solution Approach 2:
The system continuously monitors yield sensor readings and compares them against expected values derived from field models and historical data. When deviations exceed predetermined thresholds, the system automatically triggers recalibration procedures, creating a closed-loop feedback mechanism that maintains measurement precision and reliability throughout the harvesting season.
2Measurement precision
If calibration is performed frequently to maintain precision, then measurement accuracy is improved, but loss of time increases due to calibration interruptions
Solution Approach 1:
The system pre-identifies optimal calibration zones and prepares calibration parameters before actual calibration events. This allows calibration to be performed more efficiently with less time disruption, as the system already knows where to calibrate and what parameters to adjust, reducing both the frequency and duration of calibration interruptions.
Solution Approach 2:
The system dynamically adjusts calibration frequency and timing based on real-time conditions, sensor wear rates, and field variability. Rather than performing calibration at fixed intervals, the system adapts the calibration schedule to minimize time loss while maintaining precision, calibrating more frequently when conditions demand and less frequently when stability is maintained.
3Productivity
If in-situ calibration is performed during harvesting operation, then productivity is maintained, but device complexity increases due to additional calibration systems
Solution Approach 1:
The calibration system leverages existing multi-functional components already present on the harvester, such as GPS receivers, yield sensors, and communication systems. By making these components serve dual purposes (both harvesting operations and calibration), the system avoids adding significant complexity while enabling in-situ calibration that maintains productivity.
Solution Approach 2:
The system performs self-calibration using its own existing sensors and processing capabilities, rather than requiring external calibration equipment or manual intervention. The harvester uses its built-in yield sensors, GPS, and computational systems to automatically identify calibration zones, determine calibration parameters, and adjust sensor readings, thereby maintaining productivity without adding complex external calibration systems.
Data Source
AI summary
A topographical indication for a field is detected by an aerial sensor and, based on the topographical indication, an area of consistent elevation is calculated. An estimated yield indication for a field is also detected, and an area of consistent estimated yield is calculated. With a controller, a calibration candidate zone is generated, wherein the calibration candidate zone comprises an area of the field with a consistent topography and a consistent estimated yield along a width and a length of the area.