Combine Header Multisensor Monitoring for Close-Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image-based systems for detecting non-crop objects and work machine conditions, such as wear, are ineffective in detecting objects close to the combine header and miss certain machine conditions.
Innovation Solution
Implementing image-based detection systems with optical sensors and other sensors to monitor and detect non-crop objects and work machine conditions, including optical sensors positioned to capture images of crop material flow and machine components, and using processors to analyze sensor data for condition assessment and performance adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based detection systems are used to detect non-crop objects and machine conditions, then detection capability is improved, but the system fails to detect objects close to the combine header and misses certain machine conditions
Solution Approach 1:
The detection system is divided into multiple sensor types (optical sensors for distant objects, other sensors for close objects) positioned at different locations. Each sensor type handles specific detection tasks, with optical sensors capturing images of crop material flow and machine components from distance, while other sensors detect objects close to the combine header that optical sensors cannot detect.
Solution Approach 2:
The system uses multiple sensors as intermediaries to detect objects and conditions that single sensor type cannot detect. The processors integrate data from different sensor types, using each sensor as an intermediary to capture specific aspects of the harvesting environment, combining their outputs to achieve comprehensive detection.
2Reliability
If multiple sensors are used to improve detection accuracy, then detection reliability is improved, but system complexity increases
Solution Approach 1:
The processors perform multiple functions by integrating data from different sensor types. They simultaneously analyze optical sensor images for crop material flow and machine component conditions, process data from other sensors for close object detection, and generate comprehensive control signals, making the processing system multi-functional rather than requiring separate dedicated processors for each sensor type.
Solution Approach 2:
The system merges data from optical sensors and other sensors into a unified detection framework. The processors combine information from multiple sensor sources to generate comprehensive control signals, merging the detection capabilities rather than operating as separate independent systems.
Data Source
AI summary
Example apparatuses systems and methods are provided herein. In some examples, a system or method may be provided to determine one or more features of one or more combine header components, determine a condition of the one or more combine header components based at least in part on the one or more features of the one or more combine header component, determine one or more health characteristics of the one or more combine header components based at least in part on the condition of the one or more combine header components, and transmit instructions to one or more systems to adjust performance of the work machine based at least in part on the one or more health characteristics.