In-Situ Yield Map Generation for Adaptive Harvester Settings
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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 reduced 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, historical yield data, and real-time sensor inputs, allowing for automated 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 control system remain simple, but performance degrades when transitioning between areas of varying yield
Solution Approach 1:
The harvester control system transitions from fixed static settings to dynamic adjustable settings that automatically adapt to varying yield conditions. The system continuously monitors yield data and adjusts operational parameters in real-time, transforming the control system from a static to a dynamic state to maintain optimal performance across different field zones.
Solution Approach 2:
The system implements a feedback mechanism where yield data collected during harvesting is continuously fed back to the control system. This feedback loop enables the controller to analyze yield variations and automatically adjust operational settings, creating a closed-loop control system that responds to actual field conditions rather than relying on pre-set parameters.
2Reliability
If the operator manually adjusts settings when transitioning between yield areas, then performance can be maintained, but this requires continuous operator attention and intervention
Solution Approach 1:
The control system performs self-adjustment based on automatically collected yield data, eliminating the need for continuous operator intervention. The system monitors its own performance metrics and autonomously modifies operational parameters to maintain optimal harvesting conditions, making the system self-regulating rather than requiring external manual control.
Solution Approach 2:
The system replaces manual operator judgment and mechanical adjustment with automated electronic sensing and control. Yield data is captured by sensors and processed by a controller that automatically implements setting changes, substituting the operator's manual decision-making and physical adjustment actions with an automated electronic control system.
3Loss of substance
If the harvester maintains fixed operational settings, then the control system remains simple, but grain loss increases when yield conditions change
Solution Approach 1:
The system performs preliminary detection of yield conditions using sensors that continuously monitor crop characteristics before harvesting reaches problematic zones. By detecting yield variations in advance, the control system can proactively adjust settings to prevent grain loss rather than reacting after loss has occurred, implementing preventive control based on predictive yield data.
Solution Approach 2:
The control system dynamically changes operational parameters such as header height, reel speed, and rotor speed based on detected yield conditions. By adjusting these critical parameters in response to yield variations, the system optimizes harvesting efficiency and minimizes grain loss for each specific field zone rather than using fixed settings throughout.
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.