Predictive Yield Mapping for Harvester Setting Adjustment
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Solution Overview
Problem
Agricultural harvesters face performance degradation when transitioning between areas of varying yields in a field due to inadequate adjustments in machine settings, leading to issues like increased grain loss, plugging, or decreased efficiency.
Innovation Solution
The system generates a predictive yield map using in-situ sensor data and historical or vegetative index maps to anticipate crop yields, allowing for real-time adjustments in machine settings and operations to maintain optimal performance across different yield areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates with fixed settings across the field, then the machine structure and operation simplicity are maintained, but the performance degrades when transitioning between areas of varying yields
Solution Approach 1:
The harvester system dynamically adjusts operating settings (such as header height, reel speed, and rotor speed) based on real-time yield predictions from the predictive map. This dynamic adaptation allows the machine to optimize performance for each specific area encountered, resolving the contradiction between maintaining fixed simple operations and adapting to varying yield conditions.
Solution Approach 2:
The system uses in-situ sensor data combined with historical yield maps to generate real-time feedback about upcoming yield variations. This feedback loop enables the control system to proactively adjust settings before entering low-yield areas, preventing performance degradation while maintaining operational efficiency.
2Reliability
If the operator manually modifies control settings during harvesting, then the performance can be maintained in varying yield areas, but the operational complexity and time loss increase
Solution Approach 1:
The harvester system performs self-adjustment of operating parameters using automated control based on the predictive yield map. The machine monitors its own performance and automatically modifies settings without requiring operator intervention, thereby maintaining performance consistency while preserving operational simplicity.
Solution Approach 2:
The system replaces manual mechanical adjustment by the operator with an automated electronic control system that uses sensors, processors, and actuators to adjust settings. This substitution eliminates the need for manual intervention while maintaining reliable performance across varying yield conditions.
3Productivity
If the harvester speed is increased in high-yield areas, then the productivity improves, but the grain loss and plugging issues increase when transitioning to lower yield areas
Solution Approach 1:
The system applies different harvesting speeds and settings to different local areas based on the predictive yield map. In high-yield areas, the harvester operates at higher speeds with settings optimized for abundant crop, while in low-yield areas, it automatically reduces speed and adjusts settings to prevent grain loss and plugging, thereby resolving the contradiction between productivity and substance loss.
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.