Windrow Merger Predictive Mapping for Proactive Mass Flow Control
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Solution Overview
Problem
Current mobile agricultural windrowing machines operate reactively, unable to proactively adjust for variance in characteristics ahead of the machine, leading to inefficiencies in windrowing operations.
Innovation Solution
An agricultural system that generates predictive maps based on in-situ data and prior information maps, using sensors to detect mass flow and yield values, allowing for proactive control of windrowing machines by predicting mass flow and yield values at different geographic locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If windrowing machines operate reactively based on current field conditions, then the machine structure remains simple, but operational efficiency deteriorates due to inability to anticipate variations
Solution Approach 1:
The system performs preliminary actions by obtaining information maps ahead of time and using a predictive model generator to create predictive maps that forecast mass flow values at upcoming locations. This allows the windrowing machine to proactively adjust its operation before encountering field variations, improving efficiency without requiring complex real-time sensing infrastructure at every location.
Solution Approach 2:
A predictive model generator acts as an intermediary between the information maps (containing field characteristic data) and the windrowing machine control system. This intermediary processes the data to generate predictive mass flow maps, enabling the machine to anticipate variations without direct real-time sensing at every point, thus balancing complexity and operational efficiency.
2Loss of information
If real-time sensing infrastructure is deployed throughout the field, then predictive capability improves, but system complexity and cost increase
Solution Approach 1:
Instead of deploying physical sensors throughout the field, the system creates a predictive copy or representation of the mass flow distribution through predictive maps. These maps replicate the information that would be obtained from comprehensive sensing infrastructure, but are generated computationally from existing information maps and predictive models, avoiding the complexity and cost of extensive physical sensing networks.
3Manufacturing precision
If the machine adjusts operation proactively based on predictive maps, then windrow formation quality improves, but control system complexity increases
Solution Approach 1:
The control system dynamically adjusts windrowing operations based on the predictive maps. The system modifies operational parameters such as cutter head speed, reel rotation, and merger belt speed in real-time according to the predicted mass flow values at upcoming locations. This dynamic adaptation enables consistent windrow quality despite variations in crop distribution, while the control logic remains manageable through the use of pre-generated predictive guidance.
Data Source
AI summary
One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a mass flow value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive mass flow values or predictive yield values at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the mass flow value detected by the in-situ sensor or the yield value based on the detected mass flow value. The predictive map can be output and used in automated machine control.