Self-Calibrating Crop Yield Apparatus for Sensor Mismatch
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Solution Overview
Problem
Existing crop harvesting systems face inaccuracies in yield data collection due to uncalibrated or mismatched sensors, which can lead to incorrect yield estimation and management, especially when processing varies across the harvesting width.
Innovation Solution
A crop yield determining apparatus and method that automatically and continuously self-calibrates based on real-time crop conditions using a crop sensing control system, incorporating sensors and feedback devices to adjust calibration factors for precise yield estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to collect yield data across the harvesting width, then crop production information is obtained, but measurement precision deteriorates due to uncalibrated or mismatched sensors
Solution Approach 1:
The system performs self-calibration by automatically comparing sensor readings from multiple positions across the harvesting width and adjusting calibration factors without external intervention. The processor analyzes the relationship between sensors and determines calibration adjustments needed, enabling the system to self-correct measurement inaccuracies
Solution Approach 2:
The system implements continuous feedback by constantly monitoring sensor readings and comparing them against reference values or mutual sensor data. The processor uses this feedback to dynamically adjust calibration factors, ensuring measurement precision is maintained throughout harvesting operations
2Measurement precision
If data is aggregated across the harvesting width using multiple sensors, then complete yield information is obtained, but measurement precision deteriorates due to sensor mismatch and intrinsic characteristic changes
Solution Approach 1:
The system dynamically changes calibration parameters based on real-time sensor performance. The processor adjusts calibration factors for each sensor individually, compensating for mismatched characteristics and drift over time. This allows accurate aggregation of data from multiple sensors despite their inherent differences
Solution Approach 2:
The system divides the harvesting width into multiple sensor zones and calibrates each zone independently. By treating each sensor's data separately with its own calibration factor, the system prevents errors from propagating across the entire aggregated dataset, maintaining overall measurement precision
3Measurement precision
If automatic calibration is implemented based on real-time crop conditions, then yield estimation precision is improved, but device complexity increases due to continuous calibration requirements
Solution Approach 1:
The system performs preliminary calibration by establishing baseline relationships between sensors before full harvesting operations begin. This initial calibration setup reduces the complexity of continuous calibration by providing a reference framework that guides subsequent automatic adjustments
Solution Approach 2:
The calibration system transitions from static pre-set calibration factors to dynamic real-time adjustment. The processor continuously modifies calibration factors based on current crop conditions and sensor performance, enabling the system to adapt automatically without manual intervention while maintaining precision
Data Source
AI summary
A cotton harvester estimates the mass of cotton as it is harvested using sensor devices and compares the mass of each module against the estimated mass of the module as determined by the sensors so that a calibration factor may be determined and actively updated for more accurate crop yield determination. The mass flow for a specific module is accumulated and processed during harvesting using a base calibration factor and the module is weighed and compared against the expected mass using the base calibration factor to develop a candidate updated calibration factor. The base calibration factor is selectively replaced by the candidate updated calibration factor for processing a subsequent module based on machine feedback information relating to the operation of the harvester. Harvested crop data determined using the calibration factor is used to generate highly accurate yield maps.