Radar Sensor Fusion for 3D Vegetative Moisture Mapping
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Solution Overview
Problem
Existing methods for monitoring vegetative health, such as individual soil probes, are limited in providing continuous area mapping, require manual operation, and do not account for factors like pests, growth issues, or weeds, while being cumbersome and prone to damage.
Innovation Solution
A system using near-field and far-field radar, combined with multiple sensors like LIDAR, cameras, and IMUs, to create continuous three-dimensional vegetative health maps, including moisture levels, soil density, and other health indicators, which can be autonomously or remotely operated, and provide real-time data for optimized care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual soil probes are used to detect moisture levels, then moisture detection is possible, but continuous area mapping cannot be achieved
Solution Approach 1:
The patent combines multiple individual probe measurements into a unified mapping system that integrates data from numerous probes across the area to create continuous moisture distribution maps, transforming discrete point measurements into comprehensive spatial information
Solution Approach 2:
The system introduces a central processing system that acts as an intermediary between individual probes and the final mapping output, collecting, processing, and synthesizing data from multiple probes to generate continuous area-wide moisture maps
2Duration of action of stationary object
If permanent probes are installed for continuous monitoring, then moisture data can be collected, but installation becomes burdensome and probes are subject to damage
Solution Approach 1:
The system transitions from static permanent probe installations to a dynamic temporary probe system that can be deployed, used for measurement, and removed as needed, providing flexibility while maintaining continuous monitoring capabilities through multiple replaceable probes
Solution Approach 2:
The patent employs temporary, inexpensive probes that can be easily installed and removed without long-term commitment, replacing damaged or used probes with new ones to maintain monitoring continuity without the burden of permanent installation infrastructure
3Measurement precision
If manual measurement and recording is performed, then moisture values can be obtained, but labor requirements increase
Solution Approach 1:
The system implements automated data collection and recording where probes automatically measure moisture levels and the central system automatically logs, processes, and maps the data without requiring manual intervention for measurement or recording operations
Solution Approach 2:
The system establishes automated feedback loops where measurement data is immediately processed and fed back into the mapping system, enabling real-time updates and continuous monitoring without manual data entry or interpretation steps
4Measurement precision
If single-factor monitoring is performed, then specific parameter data is obtained, but comprehensive vegetative health assessment is limited
Solution Approach 1:
The system designs a multi-functional monitoring platform that can measure multiple different parameters (moisture, temperature, light, etc.) using the same infrastructure of probes and processing systems, enabling comprehensive vegetative health assessment through a single integrated system
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
Enables comprehensive, accurate, and continuous monitoring of vegetative health, allowing for precise adjustments in watering and treatment, improving plant health and reducing manual labor and equipment burdens.
Implementation Method 1
determine a moisture content of the vegetation and/or the soil by sensor data from the one or more radar sensors
Implementation Method 2
A system using near-field and far-field radar, combined with multiple sensors like LIDAR, cameras, and IMUs, to create continuous three-dimensional vegetative health maps
Data Source
AI summary
A vegetative health mapping system which creates two- or three-dimensional maps and associates moisture content, soil density, ambient light, surface temperature, and/or additional indications of vegetative health with the map. Moisture content is inferred using radar return signals of near-field and/or far-field radar. By tuning various parameters of the one or more radar (e.g. frequency, focus, power), additional data may be associated with the map from subterranean features (such as rocks, soil density, sprinklers, etc.). Additional sensors (camera(s), lidar, IMU, GPS, etc.) may be fused with radar returns to generate maps having associated moisture content, surface temperature, ambient light levels, additional indications of vegetative health (as may be determined by machine learned algorithms), etc. Such vegetative health maps may be provided to a user who, in turn, may indicate additional areas for the vegetative health device to scan or otherwise used to recommend and/or perform treatments.