Noncontact Sensor Crop Detection for Agricultural Guidance
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Solution Overview
Problem
Agricultural vehicles face challenges in accurately detecting crop rows due to crop canopies and irregular planting, leading to errors in navigation and guidance, particularly with mechanical whiskers that are prone to wear and provide false positives, and visual navigation that is hindered by crop growth and irregularities.
Innovation Solution
A noncontact sensor system that uses scan lines to detect crops without physical contact, analyzing the reflection of acoustic or electromagnetic signals to determine vehicle position and heading relative to crop rows, providing accurate guidance by processing data from multiple detected crops along a scan line.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical whiskers are used to detect crops, then contact-based detection is achieved, but the whiskers are prone to wear and damage and provide false positives
Solution Approach 1:
The patent replaces mechanical whiskers with optical sensors (cameras) that detect crops through visual imaging. The system uses image processing algorithms to identify crop rows and calculate vehicle position, eliminating mechanical contact and its associated wear and false positives from whisker deflection detection.
2Ease of operation
If visual navigation is used by operator or automated system, then guidance is provided, but accuracy is hindered by crop canopies and irregular planting
Solution Approach 1:
The system continuously captures images, processes them to detect crop rows and vehicle position, and provides real-time feedback for guidance correction. The automated steering system adjusts the vehicle path based on detected deviations from the desired course, maintaining precision despite canopy and irregular planting conditions.
Solution Approach 2:
The system performs preliminary detection and calculation of vehicle position relative to crop rows before making steering adjustments. By continuously analyzing the spatial relationship between detected crop features and vehicle location, the system proactively corrects positioning errors before they result in significant navigation deviations.
3Extent of automation
If GPS location monitoring is used, then automated steering can be implemented, but accurate detection of crops is required to determine track-angle error and cross-track distance
Solution Approach 1:
The patent uses optical sensors and image processing to detect crop rows and calculate vehicle position parameters (track-angle error and cross-track distance) instead of relying on mechanical crop contact detection. This enables automated steering with high precision by extracting geometric information from visual data about crop row locations and orientations.
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
The system enhances the accuracy and confidence of crop detection, improving navigation by reducing mechanical wear and false positives, and effectively guiding agricultural vehicles through fields with varying crop growth and irregularities.
Implementation Method 1
the one or more noncontact sensors each include: a scan line generator configured to generate the scan line... and a scan line receiver configured to receive a reflection of the scan line
Data Source
AI summary
An agricultural vehicle monitoring system includes first and second noncontact sensors configured for coupling with an agricultural vehicle, where the first and second noncontact sensors are configured to sense respective first and second environmental characteristics for determining a position of the agricultural vehicle in a field. The system further includes a comparative vehicle monitor in communication with the first and second noncontact sensors. The comparative vehicle monitor includes a filter module to filter outputs of the first and second noncontact sensors based on an indicator of a relative quality of the output of each sensor. The comparative vehicle monitor additionally includes an evaluation module to determine a vehicle position of the agricultural vehicle relative to at least one of the first and second environmental characteristics according to filtered outputs of the first and second noncontact sensors.