Hyperspectral Soil Property Estimation for Efficient Field Sampling
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Solution Overview
Problem
Traditional methods for measuring spatial variability of soil properties in agriculture, such as field grid sampling, are labor-intensive, time-consuming, and economically expensive.
Innovation Solution
Utilizing hyperspectral remotely sensed data to estimate soil properties through a computer-implemented method that includes preprocessing soil spectrum data to remove interference signals, predicting soil property datasets using soil regression modules, and selecting optimal ground sampling locations for representative soil sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If field grid sampling is used to measure spatial variability of soil properties, then measurement precision is improved, but productivity deteriorates due to labor-intensive and time-consuming procedures
Solution Approach 1:
The patent replaces the mechanical field sampling system with an optical remote sensing system. Hyperspectral sensors mounted on aircraft or satellites capture reflected light from the field, and computer vision algorithms process this optical data to estimate soil properties, eliminating the need for physical soil collection and laboratory analysis while maintaining measurement precision
Solution Approach 2:
The patent creates a digital copy of the field's soil properties through hyperspectral imaging. Instead of physically sampling soil, the system captures spectral signatures that replicate the information contained in physical samples, allowing virtual analysis of soil nitrogen, phosphorus, and other properties across the entire field simultaneously
2Measurement precision
If field grid sampling is used to measure spatial variability of soil properties, then measurement precision is improved, but loss of time increases due to extensive sampling and analysis procedures
Solution Approach 1:
The patent enables continuous monitoring of soil properties through repeated hyperspectral imaging over time. Unlike discrete sampling events, the remote sensing system can continuously capture field data as the platform passes over the area, allowing multiple measurements without interrupting field operations and providing real-time soil property information
Solution Approach 2:
The patent performs soil property assessment before planting or management decisions are made. Hyperspectral imaging can be conducted early in the season to identify spatial variability in soil nutrients, allowing farmers to pre-plan variable rate fertilizer applications or seed placement strategies based on the captured data
3Measurement precision
If field grid sampling is used to measure spatial variability of soil properties, then measurement precision is improved, but economic cost increases due to labor and analysis expenses
Solution Approach 1:
The patent makes the remote sensing system universal by designing it to measure multiple soil properties simultaneously. A single hyperspectral imaging pass captures information about nitrogen, phosphorus, moisture, and other soil characteristics across the entire field, eliminating the need for separate sampling campaigns for each property and reducing overall measurement costs
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 efficient and cost-effective estimation of soil properties without physical sampling, providing data for precise agricultural management decisions like planting, nutrient applications, and crop yield prediction.
Implementation Method 1
Utilizing hyperspectral remotely sensed data to estimate soil properties
Data Source
AI summary
A method is provided for determining soil properties for an area of land, from soil spectrum data. In an embodiment, the method includes receiving soil spectrum data records from hyperspectral sensors that represent a mean soil spectrum of a specific geo-location of the area of land and removing interference signals from the spectrum data records to create soil spectral bands. The method also includes predicting a plurality of soil property datasets based on a partial least-square regression and the soil spectral bands and selecting specific soil property datasets from the plurality of soil property datasets to represent soil properties of the specific geo-location, wherein the specific soil property datasets include property data and spectral band data for spectral bands used to determine the property data. The specific soil property datasets may then be used to generate a crop prescription of recommended hybrid seeds or population densities for the specific geo-location.