Windrower Predictive Mapping for Proactive Mass Flow Control
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Solution Overview
Problem
Current mobile agricultural windrowing machines operate reactively, unable to proactively adjust for variations in crop characteristics ahead of the machine, leading to inefficiencies in windrow formation and yield optimization.
Innovation Solution
An agricultural system that generates predictive maps using in-situ sensor data and prior information maps, such as weather, vegetative index, crop genotype, soil type, soil moisture, and soil nutrient maps, to predict mass flow and yield values, enabling proactive control of windrowing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile windrowing machines operate reactively without predictive capabilities, then the machine structure remains simple and operational complexity is low, but the machine cannot proactively adjust for variations in crop characteristics ahead of the machine, leading to inefficiencies in windrow formation and yield optimization
Solution Approach 1:
The system performs preliminary actions by generating predictive maps before the windrowing machine reaches different field locations. These maps predict mass flow and yield values based on integrated data from information maps (soil type, moisture, nutrients, weather) and real-time sensor data. The control system uses these predictions to proactively adjust operating parameters such as cutter head speed, reel rotation, and windrow placement patterns before encountering varying crop conditions, thereby optimizing windrow formation efficiency without requiring complex real-time reactive adjustments
Solution Approach 2:
The system implements feedback by continuously integrating real-time sensor data (mass flow measurements) with pre-existing information maps and using this combined information to refine predictive models. The predictive maps are updated and refined based on actual sensor measurements, creating a closed-loop system where past and present data inform future operational decisions. This feedback mechanism enables the machine to adapt to field variations while maintaining manageable operational complexity through automated control
2Measurement precision
If the machine uses only real-time sensor data without integrating prior information maps, then the system complexity is reduced, but the ability to predict yield values and optimize operations across different geographic locations is limited
Solution Approach 1:
The system merges multiple data sources including pre-existing information maps (soil type, soil moisture, soil nutrients, weather conditions, vegetative index) with real-time sensor data (mass flow measurements). These diverse data streams are integrated and processed together to generate comprehensive predictive maps that provide accurate yield predictions across different geographic locations. This merging of information layers creates a holistic view of field conditions, enabling precise yield prediction while the automated processing keeps data management complexity manageable
3Adaptability or versatility
If the windrowing machine operates without predictive maps, then the operational procedure is simple, but the adaptability to varying crop conditions across different field locations is reduced
Solution Approach 1:
The system applies local quality by generating location-specific predictive information through predictive maps that provide tailored mass flow and yield predictions for different geographic locations within the field. The control system uses these location-specific predictions to adjust operating parameters locally, allowing the machine to adapt to varying crop conditions at each position. This localized approach enables high adaptability to crop variations while maintaining relatively simple control logic through automated, position-based parameter adjustment
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. An in-situ sensor detects a mass flow value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive mass flow values or predictive yield values at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the mass flow value detected by the in-situ sensor or the yield value based on the detected mass flow value. The predictive map can be output and used in automated machine control.