Soil Clod Detection via Ray Intersection Analysis
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Solution Overview
Problem
Current methods for detecting soil clods in agricultural fields are time-consuming, expensive, and ineffective in identifying small clods or those embedded within larger clods or hollow borders.
Innovation Solution
A method and system using computer vision techniques and image processing algorithms to classify soil pixels, identify local maxima as candidate soil clods, and determine the presence of soil clods based on ray intersections and pixel heights, with the option to adjust agricultural machine operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional soil analysis methods are used to identify and characterize clods, then measurement precision may be adequate, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces traditional mechanical/manual soil analysis methods with an automated image processing system. The system uses computers to capture images of the soil surface and automatically processes these images to identify and characterize clods, eliminating the need for time-consuming manual analysis while maintaining detection accuracy.
Solution Approach 2:
The patent creates a digital copy of the soil surface through image capture. Instead of directly analyzing physical soil samples, the system captures visual representations (images) of the soil surface and processes these copies to identify clods, significantly reducing analysis time while preserving measurement precision.
2Measurement precision
If traditional clod identification methods based on a priori models are used, then large clods with closed elevation contours can be identified, but small clods and embedded clods are missed
Solution Approach 1:
The patent inverts the traditional approach by not requiring clods to fit predefined models. Instead of checking if soil features match expected clod patterns, the system identifies all potential clod candidates and then validates them, allowing detection of small clods and embedded clods that were previously missed by model-based methods.
Solution Approach 2:
The patent changes the detection parameters from requiring closed elevation contours with high gradient values to using a more flexible approach that considers local maxima and validates clod characteristics through multiple parameters including area, depth, and shape factors. This allows detection of various clod types including small and embedded clods.
3Measurement precision
If detailed soil analysis is performed to accurately characterize all clods, then detection precision improves, but data processing requirements and system complexity increase
Solution Approach 1:
The patent segments the clod detection process into distinct stages: image capture, preprocessing, clod candidate identification, validation, and characterization. Each stage processes specific aspects of the data independently, reducing overall system complexity while maintaining precision through systematic progression through the analysis pipeline.
Solution Approach 2:
The system performs self-validation through automated algorithms that check clod characteristics against predefined criteria. The computer automatically validates detected clods and filters out false positives without requiring external intervention, maintaining detection precision while simplifying the overall system architecture.
Data Source
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AI summary
In one aspect, a method for determining soil clods within a field includes receiving one or more images depicting an imaged portion of an agricultural field. The method also includes classifying a portion of the plurality of pixels that are associated with soil within the imaged portion of the field as soil pixels with each soil pixel being associated with a respective pixel height. The method also includes generating a first ray from a local maximum in a first direction and a second ray from the local maximum in a second direction that is perpendicular to the first ray until opposing endpoints are determined based on a detected edge condition. Lastly, the method includes determining whether a soil clod based at least in part the first ray or the second ray of the candidate soil clod.