Radar Vegetation Health Mapping for Continuous Moisture Detection
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Solution Overview
Problem
Existing methods for monitoring vegetative health, such as individual soil probes, are cumbersome, require manual operation, and do not provide continuous or comprehensive assessments of moisture levels or other health indicators like pests and growth issues, limiting their effectiveness in maintaining optimal vegetation conditions.
Innovation Solution
A system utilizing radar and multiple sensors to create two- or three-dimensional vegetative health maps, which includes near-field and far-field radar for continuous moisture mapping, combined with additional sensors like LIDAR, cameras, and machine learning algorithms to assess and improve vegetation health by identifying issues like weeds, pests, and growth problems, and providing recommendations for treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual soil probes are used to detect moisture levels, then moisture level indication is provided, but continuous mapping of the area is not achieved
Solution Approach 1:
The patent combines multiple soil probes into an array configuration that functions as a single integrated system. This array of probes collectively maps moisture levels across a broad area, transforming individual point measurements into continuous spatial mapping capability.
Solution Approach 2:
The invention transitions from single-point moisture measurement to two-dimensional spatial mapping by arranging probes in a grid or array pattern. This dimensional expansion allows coverage of large areas while maintaining measurement precision at each probe location.
2Duration of action of stationary object
If permanent probes are installed for moisture detection, then continuous monitoring is possible, but installation complexity and maintenance burden increase
Solution Approach 1:
The system incorporates automated data collection and transmission capabilities where the probe array self-monitors moisture levels and automatically transmits data to a central processing system, eliminating the need for manual reading and recording operations.
Solution Approach 2:
The patent replaces manual operation with electronic and computational systems. Automated sensors, wireless communication modules, and software algorithms substitute for human operators, reducing installation and maintenance complexity despite extended operational duration.
3Ease of operation
If manual probe measurement is used, then operator control is maintained, but productivity and efficiency decrease
Solution Approach 1:
The system performs automated data collection, processing, and analysis without requiring continuous operator intervention. The probe array independently monitors moisture levels, processes signals, and generates maps, dramatically improving productivity while maintaining ease of operation through simple system activation.
Solution Approach 2:
The automated system enables continuous moisture monitoring and mapping operations without interruption. Unlike manual methods that require periodic operator intervention, the automated system continuously collects and processes data, maximizing productivity while simplifying operator tasks to system oversight.
4Measurement precision
If traditional probes are used, then moisture level detection is provided, but insight into other vegetative health factors is not obtained
Solution Approach 1:
The system integrates multiple sensor types that perform different functions within a single unified platform. In addition to moisture detection, the system incorporates sensors for temperature, light, pest detection, and growth monitoring, enabling comprehensive vegetative health assessment through one versatile 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 continuous, accurate monitoring and optimization of vegetative health across large areas, reducing manual effort and providing actionable insights for improved watering, fertilization, and pest control, leading to healthier vegetation.
Implementation Method 1
At least a portion of the sensor data may be used for creating a two- or three-dimensional map of the area through which the device passes, and for determining moisture content, soil density, surface temperature, ambient light intensity, and/or additional indicators of vegetative health
Implementation Method 2
determining moisture content may be performed by use of both far-field radar, and near-field radar
Implementation Method 3
determining moisture content may be performed by use of both far-field radar, and near-field radar
Implementation Method 4
The sensor data may include data from LIDAR, radar, cameras, ultrasonic sensors, encoders, inertial measurement units, magnetometers, global positioning systems, and/or other sensors
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.