Agricultural Access Point Detection Using Image and Vehicle Data
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Solution Overview
Problem
Existing agricultural route planning techniques lack accuracy and efficiency in identifying access points to agricultural regions, which affects fuel consumption and soil compaction, and do not account for vehicle movement patterns and imagery analysis.
Innovation Solution
A computer-implemented method using machine-learning algorithms, particularly convolutional neural networks, to process agricultural region images and combine them with vehicular location data to identify access points, employing proximity validation and linear interpolation to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional route planning techniques are used, then route planning can be performed, but access point identification accuracy is poor leading to increased fuel consumption and soil compaction
Solution Approach 1:
The patent replaces traditional mechanical/geometric route planning methods with machine learning-based image processing. Convolutional neural networks analyze aerial images to automatically detect access points, replacing manual or rule-based identification systems. This substitution enables more accurate access point detection, allowing vehicles to optimize routes and reduce unnecessary travel, thereby decreasing fuel consumption.
Solution Approach 2:
The patent introduces aerial images as an intermediary medium between the route planning system and the actual terrain. By processing images to identify access points, the system creates an intermediate representation of the agricultural region that captures visual features invisible to traditional methods. This intermediary layer enables more informed route planning decisions that account for actual access point locations.
2Measurement precision
If traditional route planning techniques are used, then route planning can be performed, but poor access point identification leads to increased soil compaction
Solution Approach 1:
The patent replaces traditional mechanical route planning with machine learning-based image analysis to accurately identify access points. This enables optimization of vehicle routes to minimize unnecessary ground contact areas, thereby reducing soil compaction in non-essential regions while maintaining agricultural operational efficiency.
Solution Approach 2:
The system uses image processing feedback to continuously improve access point identification. By analyzing aerial images and comparing detected features with actual terrain characteristics, the system refines its access point detection accuracy. This feedback mechanism enables more precise route planning that accounts for soil vulnerability areas, reducing harmful compaction effects.
3Measurement precision
If machine-learning methods are used to process images, then access point identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent extracts only the essential features needed for access point identification from complex aerial images using convolutional neural networks. Instead of processing entire images or all possible features, the system focuses on extracting relevant visual patterns such as tracks, gates, and terrain transitions. This extraction approach maintains high identification accuracy while reducing processing complexity by eliminating unnecessary computational steps.
Solution Approach 2:
The system performs preliminary processing of aerial images to prepare them for access point detection. This includes pre-processing steps such as normalization, feature enhancement, and candidate region identification before applying the main detection algorithm. By performing preliminary actions, the system reduces the complexity of the main processing task while maintaining or improving identification accuracy.
Data Source
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AI summary
A mechanism for identifying access points on a boundary of an agricultural region. An image of the agricultural region is processed to identify candidate or potential access points. A location of each candidate point is identified from image location information. The candidate points are then processed to identify any access points and the location of said access points.