Windrower Predictive Mapping for Proactive Mass Flow Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mobile agricultural windrowing machines operate reactively and cannot proactively account for variance in characteristics ahead of their direction of travel, limiting the ability to adjust operating parameters effectively.
Innovation Solution
A system that generates predictive maps using in-situ data and prior or predicted data to control mobile machines, such as self-propelled windrowers, by integrating sensors to detect mass flow and yield values, and utilizing weather, vegetative index, crop genotype, soil type, soil moisture, and soil nutrient maps to adjust operating parameters proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reactive control based on current sensor data is used, then the machine can respond to current field conditions, but it cannot proactively account for variance in characteristics ahead of its direction of travel
Solution Approach 1:
The system performs preliminary actions by generating predictive maps that forecast mass flow and yield values ahead of the machine's path. These predictive maps are created by analyzing current sensor data in conjunction with historical field data, weather patterns, and crop growth models, allowing the control system to prepare adjustment strategies before encountering actual field conditions.
Solution Approach 2:
The predictive mapping system acts as an intermediary between current sensor data and future field conditions. By creating a spatial-temporal model that bridges the gap between present observations and future states, the system enables proactive control decisions based on predicted rather than merely reactive responses to actual conditions.
2Productivity
If the machine operates without predictive information, then the system complexity remains low, but the ability to adjust operating parameters proactively is limited
Solution Approach 1:
The predictive mapping system serves multiple functions simultaneously: it processes current sensor data, integrates historical field information, forecasts future mass flow and yield values, and provides control recommendations. This multi-functional approach enables proactive parameter adjustment without requiring separate systems for each function, thereby improving productivity while managing complexity.
Solution Approach 2:
The system uses its own sensor data and accumulated field knowledge to generate predictive information, making the complexity investment pay for itself through continuous self-improvement. As the system operates, it accumulates more data that refines its predictive models, allowing it to become increasingly accurate and efficient over time.
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.