Harvester Stereo Vision Control for Crop Canopy and Ground Alignment
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Solution Overview
Problem
Existing agricultural harvesters face challenges in accurately estimating ground elevation and crop yield due to limited field of view and inability to distinguish unharvested crop from the ground, leading to unreliable vehicle control.
Innovation Solution
A method utilizing stereo vision data to create three-dimensional representations of crop canopy and ground elevation, combined with GPS data to correct and align imaging data, enabling precise estimation of crop yield and ground height, and adjusting harvester settings for optimal harvesting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to estimate ground elevation, then ground elevation data can be obtained, but the estimation becomes incorrect on slopes due to limited field of view and inability to distinguish unharvested crop from ground
Solution Approach 1:
The sensor system divides the field of view into multiple segments or zones, allowing different processing methods for different regions. The controller segments the analysis to distinguish between crop canopy and ground surface in complex terrain, improving measurement precision on slopes.
Solution Approach 2:
An intermediary processing layer is introduced between the sensor and the ground elevation estimate. The controller acts as an intermediary that processes raw sensor data, applies corrections for slope conditions, and generates reliable ground elevation estimates by mediating between the sensor's limited capabilities and the required accuracy.
2Measurement precision
If stereo vision data is collected to create three-dimensional crop representation, then crop yield estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The imaging system is designed with multi-functionality, where the same stereo vision system serves multiple purposes: creating three-dimensional crop representations for yield estimation, correcting ground elevation data, and providing spatial context for harvesting operations. This reduces the need for separate specialized devices.
Solution Approach 2:
The patent combines multiple functions into a unified system. The controller merges ground elevation correction, crop yield estimation, and harvesting control into a single integrated system that processes stereo vision data comprehensively, reducing overall device complexity despite the advanced capabilities.
3Measurement precision
If GPS data is used to correct and align imaging data, then measurement accuracy is improved, but the system requires integration of multiple data sources increasing complexity
Solution Approach 1:
The system implements feedback mechanisms where GPS data continuously corrects and aligns imaging data in real-time. The controller uses feedback from GPS position and orientation information to adjust and refine the alignment of stereo vision data, maintaining high measurement precision throughout the harvesting operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of crop yield estimation and harvester control, allowing for optimized harvesting operations by compensating for ground elevation errors and adjusting harvester settings based on real-time data.
Implementation Method 1
collecting stereo vision data, by one or more imaging devices, to determine a crop three-dimensional representation
Data Source
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Figure 2B
AI summary
An upper point cloud estimator is configured to estimate a three-dimensional representation of the crop canopy based on collected stereo vision image data. A lower point cloud estimator is configured to estimate a ground three-dimensional representation or lower point of the ground based on the determined average. The electronic data processor is configured to determine one or more differences between the upper point cloud (or upper surface) of the crop canopy and a lower point cloud of the ground, where each difference is associated with a cell within a grid defined by the front region. The electronic data processor is capable of providing the differences to a data processing system to estimate a yield or differential yield for the front region, among other things.