Predictive Reel Control for Variable Field Harvesting
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Solution Overview
Problem
Agricultural harvesters face challenges in efficiently adjusting reel operations based on varying field characteristics, leading to inconsistent crop harvesting performance.
Innovation Solution
A predictive map is generated using in-situ and prior data to control reel subsystems of agricultural harvesters, adjusting parameters such as reel height, fore-to-aft position, and speed based on geographic location and field characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reel operations are adjusted based on varying field characteristics, then harvesting performance consistency is improved, but system complexity increases due to predictive map generation and dynamic control requirements
Solution Approach 1:
The system generates predictive maps before harvesting operations to forecast field characteristics such as crop density, moisture content, and yield potential at different geographic locations. This preliminary action allows the reel subsystem to be pre-configured with optimal settings for upcoming sections of the field, ensuring consistent harvesting performance without requiring complex real-time adjustments during operation.
Solution Approach 2:
The system creates a digital replica of the field's characteristics through predictive maps that store geographic location data, crop conditions, and harvesting parameters. This informational copy allows the control system to reference and adjust reel operations based on predicted field conditions without physically measuring every parameter in real-time, reducing system complexity while maintaining performance consistency.
2Productivity
If reel parameters are dynamically adjusted based on geographic location, then crop handling efficiency is improved, but control system complexity increases
Solution Approach 1:
The reel subsystem parameters such as rotational speed, finger engagement depth, and height are dynamically adjusted based on the harvester's geographic location and the predictive map data. The control system automatically modifies these parameters in real-time as the harvester moves through different field zones, optimizing crop handling efficiency for each specific location without requiring manual intervention or overly complex control mechanisms.
Solution Approach 2:
The system incorporates feedback from geographic position sensors and predictive map data to continuously monitor and adjust reel operations. The control system compares actual harvester location with predicted field characteristics and automatically modifies reel parameters to maintain optimal harvesting conditions, improving productivity through closed-loop control while managing system complexity through automated feedback mechanisms.
3Measurement precision
If predictive maps are generated using in-situ and prior data, then harvesting accuracy is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system collects and processes field data in advance to generate predictive maps that contain pre-analyzed information about crop characteristics, yield potential, and optimal harvesting parameters for different geographic locations. This preliminary data processing reduces the computational burden during actual harvesting operations, as the control system only needs to reference pre-generated maps rather than performing complex real-time analysis, thereby improving harvesting accuracy without proportionally increasing system complexity.
Data Source
AI summary
A predictive map is obtained by an agricultural system. The predictive map maps characteristic values at different geographic locations in a field. A geographic position sensor detects a geographic location of an agricultural harvester at the field. A control system generates a control signal to control a reel subsystem of the agricultural harvester based on the geographic location of the agricultural harvester and the predictive map.