Predictive Map Generator for Agricultural Harvesting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural harvesters face challenges in optimizing operations due to varying crop densities and previous agricultural operations, which affect power usage and harvesting efficiency, as existing systems lack predictive capabilities to adjust settings in real-time based on current and historical field data.
Innovation Solution
An agricultural work machine generates a predictive map by combining in-situ sensor data with prior operation maps, using a predictive model to anticipate agricultural characteristics like crop density and power requirements, enabling automated adjustments of operating parameters for optimized harvesting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time predictive control is implemented to optimize harvesting parameters, then harvesting efficiency and productivity improve, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by generating predictive maps before harvesting operations begin. These maps contain pre-calculated optimal harvesting parameters based on historical data and crop characteristics, allowing the control system to simply retrieve and apply predetermined values rather than performing complex real-time calculations during harvesting, thus improving productivity while managing system complexity
Solution Approach 2:
The system creates simplified copies of field data in the form of predictive maps that store essential harvesting information. Instead of processing raw sensor data in real-time, the system uses these pre-generated map copies containing aggregated insights and optimal parameters, reducing computational complexity while maintaining harvesting efficiency
2Measurement precision
If multiple information maps and sensor data are integrated to generate predictive maps, then measurement precision and control accuracy improve, but device complexity and data processing requirements increase
Solution Approach 1:
The system merges multiple information maps (yield maps, moisture maps, crop density maps) with sensor data into a single integrated predictive map. This consolidation approach maintains high measurement precision by incorporating all relevant data sources while simplifying the overall system architecture by presenting a unified output that the control system can directly use without managing multiple separate data streams
Solution Approach 2:
The predictive map serves as an intermediary layer between raw sensor data/multiple information maps and the control system. This intermediary processes and integrates all input data, transforming complex multi-source information into simplified predictive parameters that improve measurement precision while shielding the control system from data integration complexity
Data Source
Figure 1
Figure 2
Figure 3A
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.