Infrared Crop-Row Tracking for Real-Time Farm Vehicle Alignment
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Solution Overview
Problem
Existing autonomous driving systems in agriculture, particularly for large vehicles, are not effectively suited for realignment along crop rows and swaths using infrared cameras, as they fail to accurately detect deviations and correct alignment in real-time.
Innovation Solution
The method employs one or more long-wave infrared cameras to capture thermal images, which are processed to identify temperature maxima in pixel lines, allowing for the interpolation of homologous maxima to determine angular phase shifts and lateral deviations, enabling the vehicle to automatically correct its alignment with crop rows or swaths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing autonomous driving systems use infrared cameras for agricultural vehicles, then thermal imaging capability is provided, but real-time alignment detection precision along crop rows deteriorates
Solution Approach 1:
The system divides the thermal image into multiple horizontal pixel lines and processes each line independently to identify temperature maxima. This segmentation allows precise detection of individual crop row positions by analyzing temperature patterns in each horizontal slice of the image, thereby improving alignment detection precision.
Solution Approach 2:
The system replaces mechanical alignment measurement methods with thermal imaging-based detection. By using infrared cameras to capture thermal signatures of crops and analyzing temperature distributions in pixel lines, the system achieves non-contact, real-time detection of vehicle alignment with crop rows, enhancing both reliability and precision.
2Extent of automation
If conventional driving assistance systems are used in large agricultural vehicles, then basic navigation is provided, but real-time correction of alignment deviations along rows deteriorates
Solution Approach 1:
The system continuously captures thermal images, processes pixel lines to identify temperature maxima corresponding to crop rows, calculates angular phase shifts between the vehicle axis and crop alignment, and provides real-time feedback for correction. This closed-loop feedback mechanism enables automatic real-time alignment correction, improving ease of operation while maintaining high automation.
Solution Approach 2:
The system performs autonomous alignment detection and correction without requiring manual intervention. By automatically processing thermal images, identifying crop row positions through temperature maxima, calculating deviations, and guiding correction, the system serves itself, enhancing both automation extent and operational ease.
3Device complexity
If infrared cameras are used without advanced processing, then thermal imaging is achieved, but detection of angular phase shift and lateral deviation deteriorates
Solution Approach 1:
The system performs preliminary processing by selecting and organizing horizontal pixel lines from the thermal image before analyzing temperature patterns. By pre-processing the image data to extract relevant horizontal lines and prepare them for maxima detection, the system enables accurate detection of angular phase shift and lateral deviation without requiring overly complex hardware configurations.
Solution Approach 2:
The system transforms the two-dimensional thermal image into one-dimensional temperature profiles by analyzing horizontal pixel lines. This dimensional reduction allows the system to detect alignment parameters by examining temperature variations along each horizontal line, simplifying the detection process while maintaining or improving measurement precision for angular phase shift and lateral deviation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for precise realignment and control of the vehicle's position relative to crop rows or swaths, reducing the risk of damage and improving operational efficiency by accurately detecting and correcting deviations in real-time.
Implementation Method 1
at least one infrared camera (thermal camera) with long wave, capable of allowing at least an automatic realignment of an autonomous driving vehicle along rows of plantations and swaths
Data Source
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AI summary
Autonomous driving method in the agricultural field by means of an thermal camera comprising the procedure of obtaining an interpolating function of at least two pixels, of alignments of plants or swaths of a thermal image that appears in front of an agricultural vehicle, acquired through at least one thermal camera, said at least two pixels being corresponding to at least two homologous peaks identified in as many at least two vectors built on values of temperature intensity of corresponding consecutive pixels belonging to as many straight and horizontal lines of pixels extracted from the thermal image and a procedure for calculating an angular phase shift and/or a lateral deviation of the interpolating function with respect to a vertical axis of the thermal image.