Predictive Crop Mapping for Adaptive Harvester Control
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Solution Overview
Problem
Agricultural harvesters face challenges in optimizing performance due to variations in crop characteristics such as kernel size and biomass distribution across a field, leading to suboptimal machine settings and reduced efficiency.
Innovation Solution
The use of in-situ sensors on agricultural work machines to collect data on crop characteristics in real-time, combined with prior information maps, generates predictive maps that forecast crop characteristics across the field. These predictive maps can be used to automatically adjust machine settings for optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fixed machine settings are used, then device complexity is reduced, but harvesting efficiency and performance optimization deteriorate due to inability to adapt to varying crop characteristics
Solution Approach 1:
The patent implements dynamic machine settings that automatically adjust harvesting parameters based on real-time crop characteristics detected by sensors. The system transitions from fixed static settings to dynamic adaptive settings, allowing the harvester to optimize performance across varying field conditions without increasing operational complexity for the user.
Solution Approach 2:
The system incorporates sensor feedback loops that continuously monitor crop characteristics (kernel size, biomass distribution) and automatically adjust machine settings in response. This closed-loop feedback mechanism enables the harvester to adapt to field variations without requiring manual intervention, resolving the contradiction between automation benefits and operational simplicity.
2Adaptability or versatility
If real-time sensor data collection and predictive map generation are implemented, then adaptability to varying crop conditions improves, but device complexity increases
Solution Approach 1:
The system generates predictive maps of crop characteristics (kernel size, biomass distribution) before harvesting operations begin. This preliminary action allows the harvester to pre-plan optimal settings for different field zones, enabling adaptability to crop variations without requiring complex real-time decision-making during operation. The predictive modeling handles the complexity advance, simplifying execution phase operations.
3Productivity
If automated machine control based on predictive maps is used, then harvesting efficiency improves, but loss of information increases due to potential data processing errors
Solution Approach 1:
The system incorporates self-verification mechanisms where the automated control continuously cross-checks predictive map data against actual sensor readings during harvesting. When discrepancies are detected, the system automatically adjusts or requests re-measurement, enabling the system to self-correct potential data errors without external intervention. This maintains data integrity while preserving the efficiency benefits of automated control.
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.