Predictive Crop State Mapping for Harvester Setting Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when operating in areas with varying crop states, such as downed crops, due to the need for adjusted machine settings to prevent grain loss and optimize harvesting efficiency.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate a functional predictive crop state map, which combines vegetative index, seeding characteristic, predictive yield, and predictive biomass data to control the harvester's operations, allowing for real-time adjustments in speed, direction, and header settings based on crop state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the agricultural harvester operates at constant speed and settings throughout the field, then the operation is simple and continuous, but harvesting efficiency decreases in areas with downed crops due to performance degradation
Solution Approach 1:
The system dynamically adjusts machine settings based on real-time crop state conditions. The harvester transitions from static, constant-speed operation to dynamic speed and setting adjustments based on sensor-detected crop conditions (standing vs. downed crop), allowing optimal harvesting efficiency in varying field conditions without requiring manual operator intervention for each change.
Solution Approach 2:
The system implements a feedback loop where in-situ sensors continuously detect crop state, the control system processes this information, and automatically adjusts machine settings in response. This closed-loop feedback enables the harvester to adapt to downed crop areas by modifying speed and header configurations, resolving the contradiction between maintaining simple operation and achieving high harvesting efficiency in variable conditions.
2Productivity
If the harvester manually adjusts settings for downed crop areas, then harvesting efficiency in those areas improves, but the complexity of operation increases and grain loss may occur during adjustment
Solution Approach 1:
The harvester system performs self-adjustment of machine settings without requiring manual operator intervention. The automated control system detects crop state changes and independently modifies speed and header configurations, eliminating the time loss associated with manual adjustments while maintaining harvesting efficiency in downed crop areas.
Solution Approach 2:
The system prepares for upcoming downed crop areas by detecting crop state changes in advance and pre-adjusting machine settings before entering problematic zones. This preliminary action prevents grain loss that might occur during reactive manual adjustments, as the harvester is already optimized for the detected crop conditions when it reaches those areas.
3Reliability
If the harvester uses standard harvesting settings for the entire field, then the operation is straightforward and continuous, but grain loss increases in areas with downed crops due to inadequate crop handling
Solution Approach 1:
The system applies different machine settings to different local areas of the field based on detected crop state. Instead of uniform settings across the entire field, the harvester implements location-specific configurations (speed, header height, reel position) tailored to whether the crop is standing or downed, thereby improving crop handling quality and reducing grain loss in problematic areas while maintaining simple overall operation through automation.
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.