Machine-Learned Crop Residue Classification Model Patch Segmentation
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Solution Overview
Problem
Existing vision-based systems for estimating crop residue coverage in fields suffer from inaccuracies in crop residue estimates, which can impact soil productivity and erosion control.
Innovation Solution
A computing system utilizing a machine-learned crop residue classification model processes imagery to determine crop residue parameter values by extracting patches from image data and receiving classifications, enabling more accurate estimates and real-time control of agricultural operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If vision-based systems use computer-aided image processing techniques to estimate crop residue coverage, then the ability to monitor crop residue is improved, but the accuracy of crop residue estimates deteriorates
Solution Approach 1:
The patent divides the image into multiple patches (e.g., 100 patches per image) and processes each patch independently through the machine-learned classification model. This segmentation allows the system to handle complex image data in manageable units, improving both automation capability and estimation accuracy by reducing the computational complexity and enabling more sophisticated analysis of each local region.
Solution Approach 2:
The patent transitions from traditional computer-aided image processing parameters to machine-learned classification parameters. The machine-learned model processes patches and outputs crop residue classifications that are then aggregated to determine overall crop residue coverage. This parameter change enables the system to achieve higher accuracy while maintaining automation, as the machine-learned models can capture complex patterns that traditional methods miss.
2Device complexity
If traditional computer-aided image processing is used for crop residue estimation, then system complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent uses machine-learned classification models that have been trained on labeled image data to copy and replicate the expertise of agricultural experts in their assessments. The models learn from training data and can automatically classify crop residue conditions, achieving high measurement precision without requiring complex manual analysis systems. This copying approach simplifies the overall system while improving accuracy.
3Measurement precision
If machine-learned classification models process multiple patches from imagery, then crop residue estimation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
By segmenting the image into multiple patches and processing them in parallel through the machine-learned classification model, the system achieves accurate crop residue estimation while managing processing time. The patches can be processed simultaneously using modern computational resources, and the results are aggregated to provide overall accuracy. This segmentation strategy balances precision requirements with acceptable processing times.
Solution Approach 2:
The patent processes only the necessary portions of the image (patches) rather than the entire image at once, which optimizes processing efficiency. By focusing computational resources on specific patches that contain relevant crop residue information, the system achieves high accuracy without excessive processing time or computational resource consumption.
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. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.