Vision Guidance Crop Row Detection Using Scan Line Profiles
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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 vision guidance system that uses an imaging unit to collect data, defines a candidate scan line profile with intensity values and position data, and employs a search engine to identify a preferential scan line profile within a constrained search space, reducing processing burden and incorporating normalization techniques to enhance robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pre-processing algorithms and threshold calculations are used to accurately identify crop rows, then measurement precision is improved, but device complexity and processing burden increase
Solution Approach 1:
The patent extracts only the essential features needed for crop row identification by using a confidence module to evaluate candidate scan line profiles. Instead of applying comprehensive pre-processing algorithms, the system selectively processes only relevant image data through a simplified pipeline that calculates intensity values and evaluates profiles against threshold criteria, thereby reducing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The image processing is segmented into distinct modular stages: collecting image data, defining candidate scan line profiles, evaluating intensity values, and determining crop row positions. This segmentation allows each module to perform a specific function with optimized computational requirements, reducing overall processing burden compared to conventional holistic pre-processing approaches.
2Measurement precision
If conventional pattern recognition methods are used for crop row detection, then measurement precision is improved, but productivity decreases due to extensive processing requirements
Solution Approach 1:
The system performs partial pattern recognition by focusing only on candidate scan line profiles that meet minimum confidence criteria. The confidence module evaluates profiles selectively rather than processing the entire image dataset, and the system stops processing once sufficient crop row positions are identified, thereby improving processing speed while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary evaluation of candidate scan line profiles using a confidence module before committing to full processing. By pre-filtering candidates based on intensity variation thresholds and confidence metrics, the system prepares only the most promising profiles for detailed analysis, significantly reducing overall processing time while preserving detection accuracy.
3Measurement precision
If conventional vision systems process image data without constraints, then measurement precision is improved, but loss of time increases due to extensive processing
Solution Approach 1:
The system dynamically adjusts processing based on real-time confidence evaluations. The confidence module continuously assesses candidate profiles and adjusts the search space accordingly, concentrating computational resources on high-confidence regions while reducing processing in low-confidence areas. This dynamic approach maintains position accuracy while minimizing processing time.
Solution Approach 2:
The system changes processing parameters adaptively by adjusting confidence thresholds and intensity value criteria based on image quality and lighting conditions. The confidence module modifies evaluation parameters in real-time to optimize the balance between measurement precision and processing speed, reducing time loss while maintaining accurate crop row position identification.
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. The vision data processor defines a candidate scan line profile for a corresponding heading and pitch of associated with a directional movement of a vehicle, for example, traversing the two or more crop rows. The candidate scan line profile comprises an array of vector quantities, where each vector quantity comprises an intensity value and a corresponding position datum. A preferential scan line profile in a search space about the 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.