Crop Row Vision Using Segmented 1D Profiles for Curved Fields
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Solution Overview
Problem
Existing agricultural machine vision systems struggle with accurately identifying crop rows in curved and varying conditions, such as those with weeds, water management issues, and inconsistent plant sizes, while also requiring complex computational overhead and inefficient storage of features.
Innovation Solution
A method that processes agricultural imagery by dividing it into sections, maintaining spatial relationships along one dimension and reducing complexity along another, allowing for real-time crop row detection and guidance without assuming straight lines, and efficiently storing features using mathematical representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine vision methods are used for crop row detection, then the system is simple to implement, but it fails to accurately detect curved rows and handles agricultural variability poorly
Solution Approach 1:
The patent divides the agricultural field into multiple sections along the direction of travel, processing each section independently to maintain spatial relationships while reducing computational complexity. This segmentation allows accurate detection of curved rows in each local area without requiring the entire field to be processed as one complex unit.
Solution Approach 2:
The patent transforms the problem from two-dimensional image space to a one-dimensional profile by creating intensity profiles along columns of pixels. This dimensional reduction simplifies the detection algorithm while preserving the essential spatial information needed to identify curved row patterns, resolving the contradiction between accuracy and complexity.
2Reliability
If complex filtering algorithms are used to handle agricultural variability, then detection robustness improves slightly, but computational overhead increases significantly
Solution Approach 1:
Instead of applying complex filtering to the entire image, the patent processes only the essential one-dimensional intensity profiles extracted from column data. This partial processing approach maintains detection robustness by focusing computational resources only where needed, while significantly reducing overall computational overhead compared to full-image filtering algorithms.
3Productivity
If existing tools like OpenCV are used for feature detection, then basic row detection is possible, but efficient storage of macro features and handling of broken features is not achieved
Solution Approach 1:
The patent creates a simplified one-dimensional representation (intensity profile) that copies the essential row location information from the complex two-dimensional image data. This compressed representation stores macro feature information efficiently while maintaining the continuity and spatial relationships needed to handle broken or curved rows, overcoming the limitations of conventional feature storage approaches.
Data Source
AI summary
A method for visually identifying crop rows includes acquiring imagery from at least one imaging device operatively connected to an agricultural vehicle while the agricultural vehicle is traversing a field, the imagery includes plants arranged in rows, processing the imagery at a computing device to preserve spatial information along a first dimension while reducing data complexity along a second dimension to generate processed data, analyzing the processed data to identify locations of the rows within the field. The method may include dividing the imagery into multiple sections along a direction of travel of the agricultural vehicle and for each section, performing statistical analysis to generate a one-dimensional intensity profile, applying an edge detection operation to identify predominantly vertical features within the imagery and filtering out predominantly horizontal features to isolate plant stalks, and/or identifying spatial relationships between neighboring rows and using the spatial relationships to validate row identification.