Predictive Yield Mapping for Adaptive Harvester Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when transitioning between areas of varying yield in a field due to inadequate adjustments in machine settings, leading to issues such as increased grain loss, plugging, or decreased efficiency.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate a functional predictive yield map, which predicts crop yield based on relationships between vegetative index values and historical yield data, allowing for real-time adjustments in machine settings to optimize 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 yield
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 information map, transforming the fixed-settings operation into an adaptive dynamic system that optimizes performance across varying yield areas
Solution Approach 2:
The system uses in-situ sensors to detect actual yield values and compares them with predicted values from the information map, creating a feedback loop that continuously refines the yield predictions and adjusts machine settings to maintain optimal performance
2Adaptability or versatility
If the operator manually modifies control settings when transitioning between yield areas, then the adaptability to varying yield is improved, but the operation complexity and time loss increase
Solution Approach 1:
The harvester system automatically adjusts its own operating settings based on the information map and sensor data, eliminating the need for operator intervention and enabling the machine to self-optimize performance across varying yield areas
Solution Approach 2:
The system replaces manual operator control with an automated control system that uses electronic sensors, processors, and actuators to detect yield variations and adjust machine settings, substituting mechanical/manual operations with electronic automation
3Quantity of substance
If the harvester transitions from reduced yield to increased yield areas, then the grain harvest quantity increases, but grain loss and plugging increase due to inadequate setting adjustments
Solution Approach 1:
The system uses the information map to predict yield variations in advance and proactively adjusts operating settings before the harvester enters high-yield areas, preventing grain loss and plugging by preparing the machine in advance for the upcoming yield 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.