Predictive Harvest Control for Variable Crop Flow
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Solution Overview
Problem
Agricultural harvesters face performance degradation when encountering areas with varying vegetation, leading to non-uniform crop flow and underfeeding issues, which can cause damage and impact machine efficiency and grain quality.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate maps that predict agricultural characteristics such as crop flow and rotor drive pressure, allowing for automated control adjustments to maintain optimal performance across different field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the harvester operates through areas of varying vegetation density, then the harvesting operation can cover the entire field, but the performance of the harvester degrades due to increased material in denser vegetation areas
Solution Approach 1:
The system performs preliminary mapping of vegetation characteristics using satellite imagery and aerial photography before the harvesting operation. This advance information allows the harvester to be pre-prepared with predicted vegetation density data, enabling proactive adjustments to harvesting parameters before entering challenging areas, thus maintaining performance while covering the entire field
2Productivity
If the operator manually modifies control of the harvester upon encountering varying vegetation, then the harvester performance can be adjusted, but the response time is delayed until the variation is encountered
Solution Approach 1:
The system generates predictive maps of vegetation density and characteristics before the harvesting operation begins. This allows the operator to anticipate upcoming variations in vegetation and prepare control adjustments in advance, eliminating the delay of reactive manual modifications and maintaining optimal performance throughout the field
Solution Approach 2:
The system incorporates real-time feedback from in-situ sensors that continuously monitor actual vegetation characteristics during harvesting. This feedback is compared against the predictive maps, allowing for dynamic adjustments that maintain optimal performance while reducing the need for constant manual intervention
3Productivity
If automated machine control uses predictive maps, then the harvester can proactively adjust to varying vegetation, but the system complexity increases
Solution Approach 1:
The control system integrates multiple functions into a unified automated control platform that processes predictive maps, real-time sensor data, and executes harvesting parameter adjustments. This multi-functional integration reduces the need for separate specialized systems, managing complexity while enabling proactive adaptations to varying vegetation conditions
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.