Crop Residue Estimation via PCA Feature Reduction
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Solution Overview
Problem
Existing vision-based systems for estimating crop residue coverage in fields suffer from inaccuracies, which can lead to suboptimal agricultural operations and soil productivity issues.
Innovation Solution
A computing system that employs a machine-learned crop residue classification model in combination with principal components analysis to process image data from fields, extracting patches, generating reduced-dimensionality feature vectors, and determining crop residue parameter values for precise residue estimation and control of agricultural operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If vision-based systems use computer-aided image processing to estimate crop residue coverage, then the system can provide automated residue estimation, but the accuracy of the crop residue estimates is insufficient
Solution Approach 1:
The patent transforms image data through multiple parameter transformations: converting RGB images to LAB color space, extracting multiple types of features (color, texture, shape), and applying principal components analysis to reduce dimensionality while preserving important variations. These parameter changes enable more accurate classification of crop residue coverage while maintaining automated operation.
Solution Approach 2:
The system combines multiple classification approaches into a composite classification model that integrates different feature types (color features from LAB space, texture features, shape features) and uses ensemble methods to combine predictions from multiple classifiers. This composite approach leverages the strengths of different processing methods to achieve higher accuracy than any single method alone.
2Device complexity
If the system processes complete image data directly through classification, then the processing is simpler, but the dimensionality of the data increases computational complexity
Solution Approach 1:
The patent segments the image processing task into distinct stages: first dividing the image into patches or regions of interest, then extracting features from each segment separately, and finally aggregating results. This segmentation reduces the complexity of processing the entire image at once while managing data dimensionality through localized analysis.
Solution Approach 2:
The system applies principal components analysis to transform high-dimensional feature vectors into a lower-dimensional space by identifying and retaining only the most significant components. This dimensionality reduction technique converts complex multi-dimensional data into a more manageable form while preserving the essential information needed for accurate crop residue classification.
Data Source
AI summary
The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned crop residue classification model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. Furthermore, principal components analysis, such as projecting image patches onto Eigen-images, can be performed to reduce the dimensionality of the feature vector provided to the classification model.