Predictive Map Generation for Harvester Speed and Cut Height Control
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Solution Overview
Problem
Agricultural harvesters face performance issues due to varying conditions in the field, such as changes in biomass, crop state, topography, and soil properties, which can lead to inefficiencies in cutting height maintenance and speed control, resulting in suboptimal harvesting operations.
Innovation Solution
The system generates predictive maps, such as predictive speed maps and cut height characteristic maps, using in-situ data combined with prior information maps, to automatically control the harvester's speed and header position, ensuring consistent performance across different field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the harvester operates at constant speed through varying field conditions, then operational simplicity is maintained, but cutting height consistency and harvesting performance deteriorate
Solution Approach 1:
The system transitions from static constant-speed operation to dynamic speed adjustment based on real-time sensor data and predictive maps. The control system continuously modifies harvester speed and header position in response to varying field conditions (biomass, crop state, topography, soil properties), thereby maintaining cutting height consistency without sacrificing operational simplicity.
Solution Approach 2:
The system implements feedback control by using sensors to monitor actual field conditions and comparing them against predictive maps. The control system processes this feedback information and automatically adjusts operational parameters (speed, header position) to maintain optimal harvesting performance and consistent cutting height across varying conditions.
2Productivity
If the harvester speed is frequently adjusted to maintain constant feed rate, then harvesting performance is improved, but operational complexity increases
Solution Approach 1:
The control system performs self-service by automatically adjusting harvester speed and header position based on sensor input and predictive map data. The system autonomously manages the complexity of frequent parameter adjustments without requiring manual intervention, thereby improving harvesting efficiency while keeping the operational interface simple for the operator.
Solution Approach 2:
The system uses predictive maps generated before harvesting operations to pre-plan speed and header position adjustments. By having advance information about upcoming field conditions (from remote sensing and prior data), the control system can proactively adjust parameters to maintain constant feed rate, reducing the need for reactive corrections and simplifying real-time control decisions.
3Productivity
If remote sensing and predictive maps are used to guide harvesting, then operational performance is enhanced, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary data collection and processing by generating predictive maps before harvesting operations using remote sensing data and historical information. These predictive maps contain pre-processed information about field conditions (biomass distribution, crop state, topography, soil properties) that guides real-time harvesting decisions, reducing the complexity of on-the-fly data processing during actual harvesting.
Solution Approach 2:
The predictive map serves as an intermediary between remote sensing data and real-time harvesting control. Instead of directly processing raw sensor data during harvesting, the system uses the predictive map as a pre-computed reference that simplifies decision-making. The control system compares actual sensor readings against the predictive map and makes adjustments based on these comparisons, reducing real-time computational complexity.
Data Source
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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.