Predictive Yield Mapping for Automatic Harvester Setting Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when transitioning between areas of varying yield in a field, as existing technologies lack effective methods to adjust settings automatically in response to changing yield conditions.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate functional predictive maps, such as predictive yield maps, which predict agricultural characteristics at different geographic locations in a field, allowing for automated adjustments in machine settings during harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates with fixed settings across the entire field, then the device complexity is reduced and ease of operation is improved, but the productivity decreases when transitioning between areas of varying yield
Solution Approach 1:
The system performs preliminary mapping of yield variability across the field before harvesting begins. This advance knowledge allows the control system to pre-position harvesting parameters for different zones, enabling seamless transitions without real-time complexity. The map is created using ground-based measurements and satellite imagery, establishing a template that guides the harvester through varying yield areas.
Solution Approach 2:
The control system dynamically adjusts harvesting parameters based on the pre-created yield map. As the harvester moves through different zones identified on the map, the system automatically modifies settings such as header height, reel speed, and rotor RPM to match the expected yield characteristics of each area, maintaining optimal productivity without requiring complex real-time sensing and adjustment mechanisms.
2Productivity
If the operator manually adjusts settings when transitioning between yield areas, then the productivity is maintained, but the loss of time occurs during manual intervention and the ease of operation deteriorates
Solution Approach 1:
The control system operates autonomously by automatically selecting and applying appropriate harvesting parameters based on the pre-created yield map. The system serves itself by interpreting map data and adjusting settings without operator intervention, eliminating the time loss associated with manual adjustments while maintaining continuous optimal productivity across varying yield areas.
3Manufacturing precision
If detailed information maps are created to guide harvesting adjustments, then the manufacturing precision of harvesting parameters is improved, but the device complexity and loss of information increase
Solution Approach 1:
The field is segmented into distinct zones based on yield variability, with each zone assigned specific harvesting parameters. This segmentation approach creates manageable sections rather than requiring continuous complex adjustments, reducing overall system complexity while maintaining precise parameter control within each zone. The yield map divides the field into actionable segments that simplify the control process.
Solution Approach 2:
The system creates a simplified digital copy or representation of the field's yield characteristics through the information map. This map copy contains the essential data needed for control decisions without requiring the full complexity of the physical field variations. The copied information is sufficient for making precise parameter adjustments while avoiding the complexity of processing all raw field data in real-time.
4Ease of operation
If the harvester transitions between areas of varying yield without setting changes, then the ease of operation is maintained, but the loss of substance occurs through degraded performance and increased grain loss
Solution Approach 1:
The system performs preliminary mapping of yield variability across the field before harvesting begins. This advance knowledge allows the control system to pre-position harvesting parameters for different zones, enabling seamless transitions without real-time complexity. The map is created using ground-based measurements and satellite imagery, establishing a template that guides the harvester through varying yield areas.
Solution Approach 2:
The control system operates autonomously by automatically selecting and applying appropriate harvesting parameters based on the pre-created yield map. The system serves itself by interpreting map data and adjusting settings without operator intervention, eliminating the time loss associated with manual adjustments while maintaining continuous optimal productivity across varying yield areas.
Data Source
AI summary
One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.