Predictive Harvest Control for Variable Stalk Diameter Fields
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Solution Overview
Problem
Agricultural harvesters face challenges in efficiently harvesting crops due to varying stalk diameters in the field, which can lead to increased material other than grain (MOG) intake and grain loss if machine settings are not properly adjusted.
Innovation Solution
The use of in-situ sensors on agricultural work machines to detect stalk diameters and generate predictive maps that forecast stalk diameters at different locations in the field, allowing for real-time adjustments in machine settings such as deck plate spacing to optimize harvesting performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine settings are kept fixed during harvesting, then operation simplicity is maintained, but grain loss and MOG intake increase due to varying stalk diameters
Solution Approach 1:
The harvester system dynamically adjusts machine settings (such as deck plate spacing, reel speed, and cutter bar height) in real-time based on sensed stalk diameter variations. This transforms the static, fixed-setting operation into a dynamic adaptive process that responds to changing field conditions, thereby reducing grain loss and MOG intake while maintaining ease of operation through automated control.
Solution Approach 2:
The system employs sensors to continuously monitor stalk diameter and provides feedback to the control system. This feedback loop enables automatic adjustment of harvesting parameters to match actual field conditions, resolving the contradiction by maintaining operational simplicity while significantly reducing substance loss through closed-loop control.
2Manufacturing precision
If machine settings are adjusted manually for each condition, then harvesting precision improves, but operator workload and time consumption increase
Solution Approach 1:
The harvester system performs self-adjustment of harvesting parameters based on automated sensing and control. The machine monitors stalk diameter variations and automatically modifies its settings without requiring manual intervention, thereby maintaining high harvesting precision while eliminating the time consumption associated with manual adjustments.
Solution Approach 2:
The system replaces manual mechanical adjustment with automated electronic sensing and control. Sensors detect stalk diameter variations and the control system automatically adjusts mechanical parameters, substituting the time-consuming manual adjustment process with an automated system that maintains precision without increasing operator workload or time consumption.
3Productivity
If in-situ sensors and predictive maps are used, then harvesting performance improves, but device complexity increases
Solution Approach 1:
The control system integrates multiple functions into a single unified platform: sensing stalk diameter, generating predictive maps, determining optimal settings, and executing adjustments. This multi-functional integration improves harvesting performance while managing device complexity by consolidating operations rather than adding separate independent systems.
Solution Approach 2:
The system generates predictive maps of stalk diameter variations before harvesting begins and pre-determines optimal machine settings for different field zones. This preliminary action allows the harvester to operate with pre-optimized parameters, improving productivity while the complexity is managed through advance planning rather than real-time complex computations.
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.