Predictive Cutting Table Control for Variable Crop Heights
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Solution Overview
Problem
Agricultural harvesters face performance issues due to variations in crop height and characteristics in the field, leading to inconsistent cutting and intake of crops, which can be addressed by adjusting the cutting table position based on in-situ data and predictive models.
Innovation Solution
The system uses in-situ crop height and cutting height data in conjunction with prior or predicted data from maps to generate predictive models for controlling the cutting table position, integrating sensors and actuators to adjust the cutting table and cutter bar relative to the reel, ensuring consistent crop intake.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the cutting table position is fixed, then the device structure is simple, but the crop cutting consistency deteriorates due to variations in crop height
Solution Approach 1:
The cutting table is made dynamically adjustable through a control system that modifies its position based on detected crop height variations. The cutting table can be extended or retracted relative to the reel assembly to maintain optimal cutting geometry across fields with varying crop heights, transforming a static structure into a dynamic one that adapts to changing conditions.
Solution Approach 2:
A sensor system continuously detects crop height and provides feedback to a controller that automatically adjusts the cutting table position. This closed-loop feedback mechanism ensures that the cutting table maintains the correct relationship with the crop throughout the harvesting operation, improving cutting consistency without requiring manual intervention.
2Reliability
If the cutting table position is adjusted based on in-situ data and predictive models, then crop intake consistency is improved, but the device complexity increases
Solution Approach 1:
The system performs preliminary detection of crop height characteristics using sensors and predictive models before the harvesting operation begins or during initial phases. This advance information allows the control system to pre-adjust the cutting table position to optimal settings, ensuring consistent crop intake from the start of the operation rather than reacting to problems as they occur.
Solution Approach 2:
The harvesting system incorporates self-adjusting capabilities where the cutting table automatically modifies its own position based on real-time feedback from sensors and predictive models. This self-service mechanism eliminates the need for constant manual adjustment by the operator, maintaining reliable crop intake while reducing operational complexity.
3Productivity
If sensors and actuators are integrated to control cutting table position, then harvesting performance is improved, but the cost and complexity of the system increase
Solution Approach 1:
The sensor and actuator systems are integrated into the existing harvester platform to perform multiple functions. The same sensor array used for crop height detection can also provide data for other harvesting optimizations, while the actuator system that adjusts the cutting table may also facilitate other operational adjustments, maximizing the value of added components and reducing overall system complexity.
Data Source
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AI summary
An agricultural system that obtains a crop height value indicative of a height of crop at a field and a cut height value indicative of a height of a cutter above a surface of the field. The agricultural system further obtains a relationship that generates a value indicative of a cutting table position based on the crop height value and the cut height value. The agricultural system generates a control signal to drive movement of a cutting table of an agricultural harvester based on the value indicative of the cutting table position.