Predictive Biomass Maps for Harvester Throughput Control
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Solution Overview
Problem
Existing agricultural harvesting systems face challenges in efficiently processing varying biomass levels in fields, leading to suboptimal machine settings and reduced throughput.
Innovation Solution
The system generates predictive maps by combining in-situ sensor data with prior information maps, allowing for real-time prediction of biomass characteristics and optimal machine control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional harvesting systems use fixed machine settings, then operation simplicity is maintained, but productivity decreases due to inability to adapt to varying biomass levels
Solution Approach 1:
The system generates predictive maps beforehand that forecast biomass characteristics at different field locations. These maps are created by combining historical information maps with real-time sensor data, allowing the machine to anticipate upcoming conditions and pre-adjust settings before encountering varying biomass levels, thereby improving throughput without requiring complex real-time control adjustments
Solution Approach 2:
The harvesting system automatically adjusts its own operating parameters by utilizing the predictive maps and integrated sensor data. The control system autonomously optimizes machine settings based on predicted biomass characteristics, eliminating the need for continuous operator intervention and complex manual control mechanisms while maintaining high productivity
2Manufacturing precision
If real-time sensor data is collected and processed, then manufacturing precision of harvest quality improves, but loss of time increases due to data processing requirements
Solution Approach 1:
The system performs data processing and predictive modeling in advance to generate maps of biomass characteristics before the harvesting operation begins. By pre-processing sensor data and creating predictive models offline, the system eliminates time-consuming real-time calculations during harvesting, ensuring both high harvest quality and efficient operation without time loss
Solution Approach 2:
The system replaces complex real-time computational processing with pre-generated predictive maps and simplified lookup tables during harvesting. Instead of performing heavy data analysis in real-time, the control system references pre-computed predictive information, substituting mechanical computation with pre-prepared data structures that provide immediate guidance for precise harvest quality control
3Adaptability or versatility
If predictive maps are generated using combined sensor data and information maps, then adaptability to field conditions improves, but device complexity increases due to multiple data sources
Solution Approach 1:
The system merges historical information maps containing field characteristics with real-time sensor data to generate comprehensive predictive maps. By integrating multiple data sources into a unified predictive model, the system achieves high adaptability to varying biomass conditions while managing complexity through consolidated data structures and integrated processing workflows
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.