Vision Guidance Crop Row Identification Search Space Constraint
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Solution Overview
Problem
Conventional vision guidance systems for identifying crop rows in agricultural fields face challenges in processing capacity and noise sensitivity, requiring extensive pre-processing and being prone to image aberrations from inconsistent lighting.
Innovation Solution
A system and method that utilize an imaging unit to collect image data, define a candidate scan line profile, and search for a preferential scan line profile within a constrained search space, employing a confidence module to identify crop row positions with reduced processing burden and enhanced robustness through normalization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pre-processing algorithms (binarization, threshold calculations) are used to accurately identify crop rows, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and eliminates complex pre-processing steps (binarization, threshold calculations) from the image processing pipeline. Instead of applying multiple complex algorithms, the system directly processes raw image data using simplified scan line intensity comparisons, removing unnecessary processing stages while maintaining identification accuracy
Solution Approach 2:
The patent inverts the conventional approach by not trying to enhance or preprocess images to make crop rows more detectable. Instead, it directly analyzes the raw image data by comparing scan line intensities, working with the original image characteristics rather than attempting to transform them into an idealized form
2Measurement precision
If conventional pattern recognition methods are used for crop row detection, then measurement precision is improved, but processing speed deteriorates due to high noise sensitivity requiring extensive processing
Solution Approach 1:
The patent removes complex pattern recognition algorithms and extensive noise filtering steps from the processing pipeline. By directly comparing scan line intensities and identifying transitions, the system achieves crop row detection without the computational burden of conventional pattern recognition methods
Solution Approach 2:
The patent skips multiple intermediate processing stages (pre-processing, noise filtering, feature extraction) that conventional systems require. The system rushes through to the essential operation of comparing scan line intensities to identify crop rows, achieving rapid detection by eliminating non-essential processing steps
3Measurement precision
If the search space for crop row identification is not constrained, then measurement precision is improved by considering all possible positions, but processing time increases
Solution Approach 1:
The patent segments the image into horizontal scan lines and processes them independently in sequence. By dividing the image processing into discrete scan line segments and comparing adjacent scan lines, the system efficiently searches through all possible crop row positions without requiring exhaustive analysis of the entire image space
Solution Approach 2:
The patent performs a focused comparison of scan line intensities at specific locations to identify crop rows. Rather than analyzing all possible orientations and positions with full pattern recognition, the system applies a targeted approach by examining intensity transitions at scan line intersections, performing just enough processing to identify rows efficiently
4Reliability
If normalization techniques are not applied, then device complexity is reduced, but reliability deteriorates due to image aberrations from inconsistent lighting
Solution Approach 1:
The patent creates an equipotential processing approach by comparing relative intensities between adjacent scan lines rather than relying on absolute intensity values. This differential comparison method neutralizes the effect of varying lighting conditions, making the system robust to illumination changes without requiring complex normalization algorithms
Solution Approach 2:
The system uses feedback from adjacent scan line comparisons to adaptively identify crop row positions. By continuously comparing intensity transitions between neighboring scan lines and using these comparisons to guide row identification, the system maintains reliability under varying lighting conditions through local adaptive processing rather than global normalization
Data Source
AI summary
A system and method of identifying a position of a crop row in a field, where an image of two or more crop rows is transmitted to a vision data processor. A preferential scan line profile in a search space about a candidate scan line profile is determined, and the candidate scan line profile is identified as a preferential scan line profile for estimating a position (e.g., peak variation) of one or more crop rows if a variation in the intensity level of the candidate scan line profile exceeds a threshold variation value. Alternatively, a position datum associated with a highest intensity value within the array of vector quantities can be selected as being indicative of a candidate position of a crop row. The candidate position is then identified as a preliminary row position if a variation in intensity level of the candidate scan line profile exceeds a threshold variation value.