Predictive Field Mapping for Harvester Power Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation due to varying agricultural characteristics such as dense crop plants, weeds, crop moisture, soil properties, and topography, which increase power demands on subsystems.
Innovation Solution
The system generates predictive maps using in-situ sensor data and information maps to anticipate power characteristics, allowing for optimized machine control and resource allocation during harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates through fields with varying agricultural characteristics (dense crops, weeds, moisture variations), then the harvester can complete harvesting across the entire field, but power demand increases and performance degrades
Solution Approach 1:
The system performs preliminary mapping of agricultural characteristics (crop density, moisture, soil properties) before harvesting operations. This advance knowledge allows the harvester to predict power requirements and adjust operations proactively, preventing power shortages before they occur during high-demand areas.
Solution Approach 2:
The harvester dynamically adjusts its operating parameters (speed, header height, processing intensity) based on real-time location and predicted agricultural characteristics. This dynamic adaptation allows the machine to maintain optimal power consumption across varying field conditions while completing the full harvesting task.
2Reliability
If the harvester increases power allocation to handle dense crops and difficult conditions, then harvesting performance improves, but overall energy consumption increases
Solution Approach 1:
The system applies different power allocation strategies to different locations within the field based on local agricultural characteristics. Areas with dense crops or challenging conditions receive increased power allocation, while favorable areas use standard or reduced power settings, optimizing the balance between performance and energy consumption.
Solution Approach 2:
The harvester changes operational parameters (speed, power distribution to subsystems, processing intensity) based on predicted agricultural conditions. This parameter adaptation allows the system to maintain reliable harvesting performance in difficult areas while minimizing energy consumption in easier areas.
3Power
If real-time sensing and predictive mapping systems are implemented, then power requirements can be optimized, but system complexity increases
Solution Approach 1:
The complex system is divided into separate functional modules: mapping modules that collect agricultural characteristic data, prediction modules that analyze the data and forecast power requirements, and control modules that execute power optimization. This segmentation makes the overall complex system more manageable and maintainable.
Solution Approach 2:
The predictive mapping system acts as an intermediary layer between the harvester's sensors and its power management system. This intermediary processes raw agricultural data and translates it into actionable power optimization commands, simplifying the control architecture while enabling sophisticated power management.
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.