Predictive Machine Setting Maps for Adaptive Field Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing agricultural machines lack efficient systems for generating predictive maps that account for topographic, vegetative, optical, seeding, and soil property variations across a field, leading to suboptimal operation and control.
Innovation Solution
The system generates predictive maps using in-situ sensors and historical data to model relationships between machine settings and environmental characteristics, enabling precise control of agricultural machines based on topographic, vegetative, optical, seeding, and soil property data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional agricultural machines operate with fixed settings, then device complexity is reduced, but manufacturing precision and operational effectiveness deteriorate due to inability to adapt to field variations
Solution Approach 1:
The system performs preliminary mapping of field characteristics (topography, vegetation, soil properties) before the actual harvesting operation. This advance preparation allows the control system to have pre-computed guidance information ready, enabling precise adaptive control without requiring complex real-time decision-making during operation.
Solution Approach 2:
The system creates a digital replica or map of the field characteristics based on sensor data and historical information. This copied representation of field conditions allows the control system to reference and adapt to actual field variations without directly sensing every parameter in real-time, reducing operational complexity while maintaining precision.
2Measurement precision
If predictive maps are generated using multiple data sources (topographic, vegetative, optical, seeding, soil), then measurement precision improves, but device complexity increases due to multiple sensors and data processing requirements
Solution Approach 1:
The system employs a multi-functional control system that can process and integrate multiple types of data (topographic, vegetative, optical, seeding, soil) through a single unified platform. This universal approach allows one system to handle diverse data sources without requiring separate dedicated systems for each sensor type, managing complexity while achieving comprehensive field mapping.
Solution Approach 2:
The system introduces a data integration layer or intermediary processing system that consolidates information from multiple sensor sources. This intermediary component receives data from various sensors, standardizes formats, and synthesizes comprehensive field maps, reducing the complexity burden on individual sensors and making the overall system more manageable.
3Loss of information
If real-time sensor data is collected during machine operation, then information completeness improves, but loss of time increases due to data processing requirements
Solution Approach 1:
The system performs preliminary data processing and map generation during periods when the machine is not actively harvesting or during low-demand periods. By pre-processing sensor data and generating predictive maps in advance, the system minimizes data processing time during critical harvesting operations, ensuring information completeness without causing time loss during production.
Solution Approach 2:
The system maintains continuous operation by processing data in the background or parallel to harvesting activities. Rather than stopping to process information, the system continuously collects sensor data and updates predictive maps without interrupting the harvesting workflow, ensuring both information completeness and continuous productive action.
Data Source
Figure 1
Figure 2
Figure 3
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.