Windrower Predictive Mapping for Crop Dry-Down Control
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Solution Overview
Problem
Existing agricultural windrowing systems lack efficient methods for predicting and controlling the dry-down process of cut crops, which affects the quality and uniformity of windrows formed by machines like windrowers and balers.
Innovation Solution
An agricultural system that utilizes in-situ sensors to generate predictive maps for dry-down values, allowing for automated machine control to optimize windrow formation based on real-time sensor data and historical information maps, enabling precise windrow shaping and merging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional windrowing systems are used without predictive mapping, then the system complexity remains low, but the quality and uniformity of windrows deteriorate due to inability to control dry-down process
Solution Approach 1:
The system performs preliminary mapping of field characteristics (soil type, topography, crop variety) before windrowing operations. This advance preparation allows the control system to predict dry-down rates at different locations and adjust windrow formation parameters proactively, improving windrow quality without adding complex real-time sensing and adjustment mechanisms
Solution Approach 2:
The system creates a digital replica (information map) of the field's physical characteristics and uses this copy to simulate and predict dry-down behavior. This virtual model allows the system to optimize windrow formation parameters based on predicted conditions without requiring complex physical sensing and adjustment hardware at every location
2Measurement precision
If real-time sensor data collection is implemented to monitor dry-down, then the control precision improves, but the time required for data collection and processing increases
Solution Approach 1:
The system pre-establishes the relationship between field characteristics (soil type, topography, crop variety) and dry-down rates through information maps. During actual operations, the system only needs to query these pre-computed maps based on current location and weather conditions, rather than collecting and analyzing raw sensor data in real-time, significantly reducing processing time while maintaining accuracy
Solution Approach 2:
The system introduces an intermediate layer (predictive map generator and information maps) that translates complex sensor data and field characteristics into simplified dry-down rate predictions. This intermediary processing layer filters and pre-computes relationships, allowing the control system to make rapid decisions without directly handling raw sensor data streams
3Stability of the object's composition
If predictive mapping is used to control windrow formation, then the dry-down uniformity improves, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The system pre-determines dry-down rates for different field locations based on stable characteristics (soil type, topography, crop variety) that do not change during the harvest season. This preliminary calculation allows the system to control windrow formation using simple queries to pre-computed maps, rather than implementing complex real-time monitoring and adjustment hardware
Solution Approach 2:
The system changes the approach from directly measuring and controlling physical dry-down parameters (which would require complex sensors) to controlling windrow formation parameters (height, density, spacing) based on predicted dry-down rates. This parameter transformation allows indirect control of dry-down uniformity using simpler sensors and processing systems
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. One or more in-situ sensors detect values of one or more characteristics and generate sensor data indicative of windrow shape quality or dry down as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive windrow shape quality values or predictive dry down values at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the windrow shape quality value indicated by the in-situ sensors or the dry down value indicated by the in-situ sensors. The predictive map can be output and used in automated machine control.