Predictive Machine Setting Maps for Precision Field Operations
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Solution Overview
Problem
Agricultural machines lack efficient systems for predicting and optimizing machine settings in real-time based on varying field conditions, leading to suboptimal performance and efficiency.
Innovation Solution
An agricultural system that generates an information map of field characteristics, uses in-situ sensors to detect machine setting values, and creates a predictive map to predict machine settings at different locations in the field, enabling automated machine control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional agricultural machines operate with fixed settings, then device complexity is reduced, but manufacturing precision and operational efficiency deteriorate due to inability to adapt to varying field conditions
Solution Approach 1:
The system performs preliminary actions by generating information maps of field characteristics and creating predictive models before actual field operations. These pre-computed maps and models guide real-time machine settings, allowing the system to adapt to varying field conditions without requiring complex real-time decision-making mechanisms during operation.
Solution Approach 2:
The patent introduces an intermediary predictive map generation system that bridges the gap between simple fixed-settings operation and complex real-time adaptation. The predictive maps serve as intermediaries, translating field characteristics into optimized machine settings, thereby achieving high productivity without requiring direct complex control mechanisms during field operations.
2Manufacturing precision
If real-time sensor detection and predictive modeling are implemented, then manufacturing precision of machine settings improves, but device complexity increases due to additional sensors and processing systems
Solution Approach 1:
The system generates predictive models and information maps in advance based on historical data and field characteristics. This preliminary modeling allows the system to achieve high machine setting precision during operation by referencing pre-computed optimal settings rather than relying on complex real-time calculations, thereby reducing operational complexity while maintaining precision.
Solution Approach 2:
The predictive modeling system serves itself by using previously generated models and maps to guide current operations. The system leverages its own historical data and learned patterns to automatically determine optimal machine settings, reducing the need for external complex control inputs while maintaining high precision in machine settings.
3Productivity
If predictive maps are generated and used for automated control, then productivity increases through optimized operations, but loss of time occurs during map generation and model training
Solution Approach 1:
The system performs map generation and model training as preliminary actions before field operations begin. By completing these computationally intensive tasks in advance, the system eliminates time losses during actual operations, allowing machines to simply reference pre-computed predictive maps for real-time control, thereby maximizing operational productivity without time penalties during field work.
Solution Approach 2:
The predictive maps and models are generated periodically or batch-wise rather than continuously during operations. This periodic generation approach concentrates computational efforts in dedicated time intervals, allowing the system to maintain high operational productivity during field work while periodically updating predictive models to improve future performance.
Data Source
AI summary
An information map is obtained by an agricultural system. The information map maps values of a characteristic to different geographic locations in a field. An in-situ sensor detects machine setting values as a mobile machine moves through the field. A predictive map generator generates a predictive map that predicts the machine setting at different locations in the field based on a relationship between the values of the characteristic and the machine setting values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.