Predictive Header Map for Agricultural Harvester Control
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Solution Overview
Problem
Agricultural harvester performance is affected by varying topographical conditions, such as slope, which can lead to suboptimal header settings, resulting in inefficient crop engagement and harvesting operations.
Innovation Solution
A system that generates a predictive header characteristic map using in-situ data and prior topographic maps to adjust header settings like height, tilt, and roll in real-time, ensuring optimal engagement with crops across different terrain conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates on varying topographical conditions without real-time adjustment, then the device complexity is reduced, but the harvesting efficiency and crop engagement deteriorate
Solution Approach 1:
The system receives and processes topographic map data before the harvesting operation begins, pre-calculating the predicted header characteristics needed for different sections of the field. This allows the control system to have adjustment parameters ready in advance, improving harvesting efficiency without requiring complex real-time calculations during operation.
Solution Approach 2:
The system uses the harvester's own sensors to detect actual header characteristics and automatically compares them with predicted values from the topographic map. The control system then autonomously adjusts header settings based on this comparison, enabling self-regulation without requiring constant operator intervention or complex external control systems.
2Productivity
If the header settings are manually adjusted for each topographical change, then the harvesting efficiency improves, but the loss of time and operator workload increase
Solution Approach 1:
The system continuously monitors actual header characteristics using onboard sensors and compares them with predicted characteristics from the topographic map. This feedback loop enables automatic detection of deviations and triggers appropriate adjustments without requiring operator time for manual monitoring and adjustment.
Solution Approach 2:
The system replaces manual mechanical adjustment of header settings with automated control based on topographic data and sensor feedback. The control system automatically actuates header adjustment mechanisms, eliminating the need for operators to physically intervene for each topographical change.
3Ease of operation
If the header settings are not adjusted for topographical variations, then the ease of operation is maintained, but the grain loss and harvesting quality worsen
Solution Approach 1:
The system automatically maintains optimal header settings by using sensor feedback to detect actual characteristics and comparing them with predicted values from the topographic map. The control system autonomously makes adjustments to prevent grain loss, maintaining operational simplicity while ensuring harvesting quality without requiring operator knowledge or intervention.
Solution Approach 2:
The system pre-processes topographic map data to predict optimal header characteristics for different field sections before harvesting begins. This allows the control system to have adjustment parameters ready, automatically maintaining optimal settings throughout the operation without requiring real-time operator decisions.
4Manufacturing precision
If real-time sensor data is collected and processed, then the manufacturing precision of header settings improves, but the use of energy and device complexity increase
Solution Approach 1:
The system performs computationally intensive processing of topographic map data and prediction calculations before the harvesting operation begins. This preliminary processing reduces the computational burden during actual harvesting, allowing precise header setting adjustments with minimal real-time energy consumption.
Solution Approach 2:
The system uses sensor feedback to detect actual header characteristics and compares them with pre-calculated predicted values from the topographic map. This feedback mechanism enables precise adjustments using relatively simple real-time computations, minimizing energy consumption while maintaining high precision in header settings.
Data Source
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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.