Image-Based Irrigation Route Adjustment
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Solution Overview
Problem
Existing pivot irrigation systems face challenges in remote monitoring and control due to complexity in determining angular orientation, requiring on-site programming and recalculations, which limits efficiency and convenience for operators managing multiple systems.
Innovation Solution
Implementing an image-based irrigation system management method using machine vision analysis of sensor signals to determine crop identifiers and dynamically adjust irrigation routes in real-time, allowing for remote control and optimization of irrigation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS-based solutions are used to determine angular orientation, then remote monitoring capability is improved, but device complexity increases due to constant recalibration requirements
Solution Approach 1:
The patent replaces complex GPS-based angular orientation determination with a simpler camera-based image recognition system. Instead of using GPS coordinates and performing complex recalculations of azimuth angles, the system uses image processing to directly identify crop types and determine irrigation parameters, thereby reducing device complexity while maintaining remote monitoring capability
Solution Approach 2:
The system creates a visual copy (image) of the crop field and uses machine learning algorithms to analyze this copy for crop identification. This eliminates the need for physical GPS receivers and complex coordinate-based orientation calculations, simplifying the overall system architecture
2Adaptability or versatility
If field-based programming is used for irrigation control, then system adaptability is improved, but ease of operation deteriorates due to on-site requirements
Solution Approach 1:
The irrigation system performs self-diagnosis and self-adjustment by automatically capturing images of the crop field, analyzing crop types through machine vision, and adjusting irrigation parameters without requiring operator intervention. The system serves itself by making real-time decisions based on visual crop assessment
Solution Approach 2:
The system continuously captures images of the crop field and uses this visual feedback to automatically adjust irrigation parameters. The closed-loop feedback mechanism allows the system to adapt to changing crop conditions in real-time without requiring on-site programming or manual recalibration
Data Source
AI summary
A system and method for image-based irrigation system management. The method includes continuously obtaining sensor signals, wherein the sensor signals include at least one first image showing at least one crop; analyzing the obtained sensor signals, wherein the analysis includes performing machine vision on the at least one first image; determining, based on the analysis, at least one current crop identifier of the at least one crop; and determining, in real-time, a dispersal route for an irrigation system based on the at least one current crop identifier.