Agricultural Machine Speed Control via Predicted Field Efficiency Zones
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Solution Overview
Problem
Conventional agricultural machine sensor systems are reactive and unable to adequately respond to sudden changes in field conditions, leading to inefficiencies and reduced effectiveness in agricultural operations.
Innovation Solution
A system and method that predict field conditions by monitoring operating parameters during a first pass across a field, generating a field map with efficiency zones, and pre-emptively adjusting travel speed for an adjacent swath based on recorded travel speeds associated with those zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sensor systems are used to detect field conditions, then the system can respond to detected conditions, but the response is inherently reactive and cannot adequately respond to sudden or immediate changes in field conditions
Solution Approach 1:
The system performs preliminary actions by detecting field conditions in advance of the agricultural implement's path and pre-emptively adjusting operating parameters before the implement encounters those conditions. This eliminates the reactive delay by acting in advance based on predicted field conditions ahead of the implement.
Solution Approach 2:
The field is segmented into multiple zones ahead of the implement, with each zone having predicted field conditions. The system divides the upcoming field path into discrete segments and prepares appropriate parameter adjustments for each segment before the implement reaches them, enabling proactive rather than reactive response.
2Productivity
If the agricultural machine operates at constant speed, then operational simplicity is maintained, but efficiency and effectiveness are reduced due to inability to adapt to varying field conditions
Solution Approach 1:
The system transitions from constant speed operation to dynamic speed adjustment by continuously varying the agricultural machine's operating parameters based on predicted field conditions. The controller dynamically modifies speed, downforce, and other parameters in real-time as the machine moves through different field zones with varying conditions.
Solution Approach 2:
The system implements feedback control by continuously monitoring actual field conditions via sensors, comparing them to optimal parameters, and automatically adjusting operating parameters accordingly. This closed-loop feedback enables efficient adaptation to varying field conditions while maintaining automated control to manage system complexity.
3Productivity
If manual adjustment of operating parameters is used, then system simplicity is maintained, but operational effectiveness is reduced due to inability to adequately respond to field condition variations
Solution Approach 1:
The system enables self-service operation by automatically detecting field conditions and adjusting its own operating parameters without operator intervention. The agricultural machine monitors its own performance and autonomously modifies speed, downforce, and other parameters to optimize operation across varying field conditions.
Solution Approach 2:
The system implements feedback control by continuously monitoring actual field conditions via sensors, comparing them to optimal parameters, and automatically adjusting operating parameters accordingly. This closed-loop feedback enables efficient adaptation to varying field conditions while maintaining automated control to manage system complexity.
Data Source
AI summary
In one aspect, a method for pre-emptively adjusting machine parameters based on predicted field conditions may include monitoring an operating parameter of as the agricultural machine makes a first pass across a field. The method may also include initiating active adjustments of the travel speed of the machine based on the operating parameter as the machine makes the first pass. Furthermore, the method may include generating a map based on the travel speed and the operating parameter that included efficiency zones associated with one or more travel speeds. Moreover, the method may include determining predicted efficiency zones for an adjacent second swath within the field based on efficiency zones of the first swath within the field map. Additionally, the method may include pre-emptively initiating adjustments of the travel speed as the machine makes a second pass across the field based on the efficiency parameter for each predicted efficiency zone.