Harvester MOG Imaging Control for Straw Length Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to adequately monitor the quality of material other than grain (MOG) dispersed by agricultural harvesting machines, such as straw, which is crucial for future farming operations and adjustments.
Innovation Solution
A system utilizing an imaging device and controllers to capture and analyze image data from multiple orientations, identify material components, determine their characteristics, and generate control signals for adjusting the harvesting machine operations based on material quality measures, particularly the length of components visible on the surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single image orientation is used for analysis, then the system complexity is reduced, but the measurement precision of material length is insufficient because components lower in the pile are obscured
Solution Approach 1:
The patent applies dimensionality change by analyzing images at multiple orientations (rotating the image through different angles) rather than a single fixed orientation. This allows the system to capture material components that are obscured in any single view, thereby improving measurement precision without requiring physically complex multi-camera setups.
Solution Approach 2:
The system performs preliminary action by pre-processing images to identify and select the optimal orientation before conducting the final length measurement. This preliminary orientation selection step ensures that the most representative view of material components is chosen, improving measurement accuracy while keeping the overall system manageable.
2Reliability
If multiple image orientations are analyzed, then the reliability of material quality measure is improved, but the processing time and computational load increase
Solution Approach 1:
The system applies partial action by analyzing a limited set of discrete orientations (e.g., 0°, 45°, 90°, 135°) rather than continuously scanning all possible angles. This provides sufficient reliability for material quality assessment while significantly reducing computational load and processing time compared to exhaustive analysis.
Solution Approach 2:
The system performs preliminary action by quickly evaluating multiple orientations to identify the optimal one before conducting detailed measurements. This preliminary screening step filters out orientations that do not provide useful information, reducing the overall processing time while maintaining reliability.
3Quantity of substance
If material components obscured by other material are included in analysis, then the quantity of analyzed components increases, but the measurement accuracy decreases
Solution Approach 1:
The patent uses dimensionality change by rotating the image through multiple orientations to find views where material components are not obscured. This allows the system to access and measure components that would be hidden in a single orientation, increasing the effective quantity of analyzable components while maintaining measurement accuracy.
Solution Approach 2:
The system applies extraction by isolating and selecting only those material components that are clearly visible and not obscured by other material in the optimal orientation. This filtering process ensures that only high-quality measurements are included, maintaining precision while still analyzing a sufficient quantity of components.
Data Source
AI summary
Systems and methods are provided for monitoring operation of an agricultural harvesting machine. Image data is obtained and used to identify one or more material components within the image data in each of a plurality of image orientations. For each image orientation and for at least one of the one or more identified material components, one or more material characteristics are determined and used to select a primary image orientation from the plurality of image orientations. A material quality measure may then be determined for identified material component(s) in the primary image orientation and operation of one or more operable components of or otherwise associated with the harvesting machine can be controlled accordingly.