Vision Guidance Crop Row Identification Scan Line Profiles
Find Innovative SolutionsGenerate Solutions
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 vulnerable to image aberrations from inconsistent lighting.
Innovation Solution
A system and method that utilize an imaging unit to collect data, define candidate scan line profiles, and employ a search engine and confidence module to identify preferential scan lines, reducing processing burden and enhancing robustness through normalization techniques and constrained search spaces.
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 only the essential features needed for crop row identification by using scan line profiles that capture intensity variations across the field of view. Instead of processing entire images through complex algorithms, the system extracts one-dimensional intensity profiles along scan lines, significantly reducing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent transforms the image processing problem from two-dimensional image analysis to one-dimensional intensity profile analysis. By changing the parameter representation from full image data to scan line intensity profiles, the system reduces computational burden while preserving the critical information needed for crop row detection.
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 extracts only the necessary intensity information along scan lines rather than processing complete images. This extraction approach maintains detection accuracy by preserving intensity variation patterns while dramatically reducing the data volume requiring processing, thereby increasing processing speed and productivity.
Solution Approach 2:
The patent applies partial action by processing only the essential scan line profiles needed for crop row identification rather than performing exhaustive image analysis. This selective processing approach maintains sufficient detection accuracy while significantly improving processing throughput and productivity.
3Measurement precision
If conventional vision systems process complete image data, then measurement precision is improved, but use of energy increases due to higher processing burden
Solution Approach 1:
The system extracts minimal sufficient data in the form of scan line intensity profiles rather than processing complete images. This extraction strategy maintains position identification accuracy by preserving critical intensity variation information while minimizing the energy required for data processing.
Solution Approach 2:
The patent changes the data representation parameter from full image matrices to one-dimensional intensity profiles. This parameter transformation reduces the computational workload and associated energy consumption while maintaining the precision needed for 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 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. In addition, a template scan line profile may be utilized, where a candidate scan line profile is identified to be a preferential scan line profile if it is consistent with the template scan line profile.