Predictive Header Control for Harvester Topography Changes
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Solution Overview
Problem
Agricultural harvesters face performance issues due to varying field topography, which affects header characteristics such as height and tilt, leading to suboptimal crop engagement and harvesting efficiency.
Innovation Solution
A system that generates a predictive header characteristic map using in-situ data and prior topographic data to control agricultural machines, adjusting header settings based on real-time field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates through fields with varying topography, then the harvester can cover more ground and maintain productivity, but the header characteristics (height and tilt) become suboptimal leading to poor crop engagement
Solution Approach 1:
The header characteristics (height and tilt) are made dynamically adjustable through automated control systems that respond to real-time topographic data from information maps and in-situ sensors. This allows the header to adapt its configuration continuously as the harvester moves through varying terrain, maintaining optimal crop engagement across different field conditions while preserving productivity.
Solution Approach 2:
The system incorporates feedback loops where in-situ sensors detect actual header characteristics and crop engagement quality, compare them against optimal values from predictive maps, and automatically adjust header settings. This closed-loop control ensures reliable crop engagement even when operating through diverse topography at high productivity levels.
2Reliability
If the operator manually adjusts header settings during harvesting, then crop engagement can be optimized for current conditions, but time is lost during adjustments and overall productivity decreases
Solution Approach 1:
The harvester system performs self-adjustment of header characteristics using automated control systems that process topographic data and sensor information to modify header height and tilt without operator intervention. This eliminates time losses associated with manual adjustments while maintaining optimal crop engagement, thereby preserving productivity.
Solution Approach 2:
The system pre-calculates optimal header settings based on predictive maps generated from field topography data before the harvester reaches specific locations. This allows the control system to be prepared with adjustment commands in advance, enabling smooth transitions without operational delays and maintaining continuous harvesting productivity.
3Reliability
If automated control systems adjust header settings in real-time, then crop engagement is optimized across varying topography, but the system complexity increases
Solution Approach 1:
The control system integrates multiple functions into a unified platform that processes topographic map data, receives in-situ sensor inputs, generates predictive models, and executes header adjustments all through a single automated control architecture. This multi-functional approach manages system complexity by consolidating control tasks rather than requiring separate systems for each function.
Solution Approach 2:
The system introduces an intermediary control layer that translates complex topographic information and sensor data into simplified control commands for header actuators. This intermediary processing layer manages the complexity by abstracting the relationship between field conditions and required mechanical adjustments, making the overall system more manageable while maintaining reliable crop engagement.
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.