Predictive Field Mapping for Harvester Power Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation due to varying conditions such as dense crops, weeds, soil properties, and topography, which increase power demands and affect efficiency, as existing systems lack effective predictive control mechanisms to manage these factors in real-time.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate predictive maps that anticipate power requirements based on agricultural characteristics like vegetative index, crop moisture, soil properties, and topography, enabling automated control adjustments during harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the harvester operates through fields with varying agricultural conditions (dense crops, weeds, soil properties, topography), then the harvester must process diverse crop conditions, but power demands increase and efficiency degrades
Solution Approach 1:
The system performs preliminary mapping of field conditions (crop density, moisture, topography, soil properties) before harvesting operations. This advance knowledge allows the harvester to pre-plan power allocation and operational parameters for different field zones, avoiding reactive power management that would consume more energy.
Solution Approach 2:
The harvester dynamically adjusts operational parameters (header height, rotor speed, fan speed, ground speed) based on real-time feedback from field condition maps and actual crop conditions. This dynamic adaptation optimizes power consumption by matching engine output to actual processing requirements in different field zones.
2Productivity
If the harvester processes dense crops and weeds, then harvesting completeness improves, but power demand increases
Solution Approach 1:
The system applies different processing intensities to different field zones based on local crop conditions. In areas with dense crops or weeds, the system increases processing parameters (header height, rotor speed) to ensure complete harvesting. In lighter areas, parameters are reduced to conserve power, achieving local optimization of the productivity-power tradeoff.
3Measurement precision
If real-time sensor data is collected and processed, then predictive control accuracy improves, but system complexity increases
Solution Approach 1:
The control system is segmented into modular components: in-situ sensors for data collection, separate mapping modules for spatial data processing, predictive modeling components for power requirement estimation, and control modules for actuator adjustment. This segmentation reduces overall system complexity while maintaining high measurement and predictive accuracy.
4Productivity
If automated control adjustments are made based on predictive maps, then operational efficiency improves, but control system complexity increases
Solution Approach 1:
The system implements closed-loop feedback where in-situ sensors continuously monitor actual crop conditions and power consumption, compare them against predictive map expectations, and automatically adjust operational parameters. This feedback mechanism achieves automated efficiency improvements while using standardized control algorithms that manage system complexity.
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.