Predictive Field Mapping for Harvester Feed Rate and Header Control
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Solution Overview
Problem
Agricultural harvesters face performance issues due to varying conditions in fields, such as changes in biomass, crop state, topography, and soil properties, which can lead to inefficiencies in harvesting operations, particularly in maintaining a constant feed rate and adjusting header heights effectively.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate predictive maps that anticipate agricultural characteristics, allowing for real-time adjustments in machine speed and header height control, thereby improving operational efficiency and maintaining performance across different field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional harvesting operations are used without predictive mapping, then the system is simpler and easier to operate, but the harvester cannot maintain consistent feed rate or adjust header heights effectively under varying field conditions
Solution Approach 1:
The system performs preliminary mapping of field conditions (biomass, topography, soil properties) before harvesting operations. This advance knowledge allows the harvester to predict and prepare for varying conditions, maintaining consistent feed rates and adjusting header heights proactively rather than reactively, thereby improving productivity without requiring complex real-time decision-making systems.
Solution Approach 2:
The system creates predictive maps that are copies or representations of actual field conditions. These maps serve as simplified models that the control system can process easily, allowing complex field variations to be represented in a manageable format that improves harvesting efficiency without requiring the full complexity of the physical field environment to be processed in real-time.
2Productivity
If real-time sensor data and predictive maps are used to maintain constant feed rate, then harvesting efficiency improves, but the device complexity increases due to additional sensors and control systems
Solution Approach 1:
The system implements feedback loops where in-situ sensors continuously monitor actual feed rate and compare it against target values derived from predictive maps. The control system automatically adjusts harvesting parameters based on this feedback, maintaining consistent feed rates through closed-loop control rather than open-loop predictions, which improves productivity while keeping the control architecture manageable.
Solution Approach 2:
The system enables the harvester to self-adjust its operating parameters based on predictive maps and sensor feedback. The automated control system performs header height adjustments and feed rate modifications without operator intervention, allowing the machine to service itself and maintain optimal performance without requiring complex manual control interfaces or operator training.
3Reliability
If automated header height adjustment is implemented using predictive maps, then mechanical stress is reduced and efficiency improves, but the device complexity and control requirements increase
Solution Approach 1:
The system uses predictive maps to identify upcoming variations in field conditions (topography, biomass density) before the harvester encounters them. The control system pre-adjusts header heights in anticipation of these conditions, preventing excessive mechanical stress before it occurs rather than reacting to stress after it develops. This proactive approach improves reliability while using relatively simple predictive algorithms.
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.