Monocular Crop Row Guidance Using FFT-Based Row Center Detection
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Solution Overview
Problem
Current crop row guidance systems relying on computer vision are complex and resource-intensive, struggling with variance in crop colors, textures, and ambient lighting, making them difficult to generalize across different crops and field conditions.
Innovation Solution
The development of crop row guidance systems using a monocular camera and signal processing techniques, including Fourier transforms and bandpass filters, to locate crop row centers and provide guidance with less expensive and complex computing hardware, such as a central processing unit (CPU), which is less resource-intensive and more adaptable to varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision systems are used for crop row guidance, then guidance accuracy can be achieved, but system complexity and computing resource requirements increase significantly
Solution Approach 1:
The patent extracts the essential feature of crop rows (周期性 pattern) and isolates it for analysis by transforming image data into frequency domain through FFT, separating the periodic row pattern from other visual noise and variations in crop appearance
Solution Approach 2:
The patent replaces complex computer vision algorithms with signal processing techniques (FFT and bandpass filtering), substituting mechanical/image-based analysis with mathematical frequency-domain analysis that is computationally simpler and more efficient
2Measurement precision
If deep learning methods are used to classify crop pixels individually, then crop row detection can be performed, but computing resources and processing time increase
Solution Approach 1:
The patent exploits the periodic nature of crop rows by applying FFT to detect the dominant frequency corresponding to row spacing, using the inherent periodicity to simplify detection from individual pixel classification to frequency pattern recognition
Solution Approach 2:
The patent transforms the problem from spatial domain (individual pixel coordinates) to frequency domain (dominant frequency), changing the parameter space to exploit the periodic characteristics of crop rows and enable faster processing
3Adaptability or versatility
If computer vision masking solutions are used, then crop row location can be identified, but the system becomes difficult to generalize across different crops and field conditions
Solution Approach 1:
The patent creates a universal solution that works across different crop types by detecting the fundamental periodic pattern rather than relying on crop-specific visual features, making the system adaptable to various row crops through a single frequency-domain approach
Solution Approach 2:
The patent changes the analysis parameter from appearance-based features (color, texture) to structure-based features (periodic spacing), enabling the system to generalize across different crops while maintaining reliability through consistent geometric patterns
Data Source
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AI summary
Described herein are technologies for guiding an agricultural vehicle (1..N) through crop rows using a camera (116, 118, 119) and signal processing to locate the crop row or centers of the crop row. The signal processing uses a filter to filter data from images captured by the camera and locates the row or the centers based on the filtered data. The filter is generated based on a signal processing transform and an initial image of the crop row captured by the camera. The filter is applied to subsequent images of the crop row captured by the camera. In some embodiments, the camera includes one lens. For example, monocular computer vision is used in some embodiments. Also, in some embodiments, a central processing (102) unit generates the filter based on the transform and the initial image of the crop row and applies the generated filter to the subsequent images of the row.