Agricultural Access Point Detection Using Image-Based Boundary Analysis
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Solution Overview
Problem
Existing agricultural route planning techniques lack accuracy in identifying access points, which affects the efficiency and sustainability of agricultural processes.
Innovation Solution
A computer-implemented method using image processing and machine-learning algorithms, particularly convolutional neural networks, to identify access points in agricultural regions, combined with vehicular location data for validation, enhances the accuracy of access point detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional route planning techniques are used, then the planning process is simple, but the accuracy of access point identification is poor
Solution Approach 1:
The patent replaces traditional mechanical/manual access point identification methods with automated image processing and machine learning systems. Satellite images and aerial photography are processed through convolutional neural networks to automatically detect and identify access points, eliminating the need for manual surveying and significantly improving identification accuracy while reducing human labor requirements.
Solution Approach 2:
The patent introduces image processing algorithms and machine learning models as intermediary systems between raw satellite/aerial imagery and access point identification results. These intermediaries automatically extract features, detect patterns, and identify access points from complex images, serving as a bridge that transforms unprocessed visual data into actionable geographic information with high precision.
2Productivity
If manual access point identification is used, then the system complexity is low, but the route planning efficiency is poor
Solution Approach 1:
The patent implements a self-service automated system where satellite images and aerial photography are automatically processed through machine learning algorithms without requiring manual intervention. The convolutional neural networks autonomously detect, analyze, and identify access points from the imagery, enabling the system to perform route planning准备工作 independently and significantly improving productivity through continuous automated operation.
Solution Approach 2:
The patent performs preliminary automated identification of access points using image processing and machine learning before route planning begins. By pre-processing satellite and aerial images to detect and catalog all potential access points in advance, the system prepares comprehensive geographic data that accelerates subsequent route planning operations and enables more efficient agricultural vehicle navigation.
3Measurement precision
If automated image processing is implemented, then access point detection accuracy improves, but computational resources required increase
Solution Approach 1:
The patent applies partial processing strategies where machine learning models focus on detecting only the most relevant features and access points rather than analyzing every pixel in detail. The convolutional neural networks are trained to identify key patterns and characteristics that indicate access points, performing sufficient analysis to achieve high accuracy while avoiding excessive computational overhead from processing all image data at maximum resolution.
Data Source
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.