Hyperspectral Soil Mapping for Precise Field Sampling
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Solution Overview
Problem
Traditional methods for measuring spatial variability of soil properties, such as field grid sampling, are labor-intensive, time-consuming, and economically expensive.
Innovation Solution
A computer-implemented method using hyperspectral remotely sensed data to estimate soil properties by preprocessing soil spectrum data to remove interference signals, selecting optimal spectral bands, and predicting soil property datasets, which are then used to create soil property data models for determining optimal sampling locations and providing recommendations for agricultural management.
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 electromagnetic sensing system. Hyperspectral sensors mounted on agricultural machinery capture soil spectral signatures remotely, eliminating the need for manual soil collection and laboratory analysis. This substitution maintains measurement precision while dramatically improving productivity by enabling continuous, real-time soil property assessment across the entire field.
Solution Approach 2:
The patent creates spectral copies of soil properties instead of physical soil samples. By capturing hyperspectral reflectance data from the soil surface, the system generates digital representations of soil characteristics (organic matter, moisture, texture) that can be analyzed remotely. This copying approach preserves measurement accuracy while eliminating the labor-intensive sampling and shipping processes.
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 implements continuous soil assessment by mounting hyperspectral sensors on moving agricultural machinery. As the machinery traverses the field, sensors continuously capture spectral data along the entire path, enabling real-time soil property mapping. This continuous measurement approach eliminates the discrete, time-consuming sampling intervals of traditional grid methods while maintaining spatial precision.
Solution Approach 2:
The patent performs preliminary soil characterization before crop planting by capturing hyperspectral data during field preparation activities. This advance knowledge of soil properties (organic matter content, moisture levels, texture) allows farmers to make informed decisions about seed placement, fertilizer application, and irrigation strategies before the growing season begins, eliminating the need for time-consuming mid-season soil testing.
3Measurement precision
If field grid sampling is used to measure spatial variability of soil properties, then measurement precision is improved, but device complexity increases due to multiple sampling equipment and analysis tools
Solution Approach 1:
The patent employs a universal hyperspectral sensing platform that can assess multiple soil properties simultaneously. A single hyperspectral sensor system captures reflectance data across hundreds of narrow spectral bands, enabling concurrent measurement of organic matter, moisture content, soil texture, and other parameters. This multi-functional approach replaces the need for multiple specialized sampling devices and laboratory analysis tools, reducing overall system complexity while maintaining comprehensive measurement precision.
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, allowing for precise agricultural decisions such as planting, nutrient applications, and crop health monitoring.
Implementation Method 1
Hyperspectral remote sensing data is used to estimate soil properties
Data Source
AI summary
A computer-implemented method is provided for facilitating agricultural operations in an agricultural field. In one example embodiment, the method includes identifying, based on spatial sampling of soil spectrum data for an agricultural field, ground sampling locations within the field to obtain physical soil samples representative of soil makeup for the field. The method also includes generating a soil model particular to the field by correlating soil properties for soil included in the soil samples obtained from the identified ground sampling locations to particular soil spectral bands included in the soil spectrum data for the field at the corresponding ground sampling locations. The method then further includes compiling, using the soil model, a soil map of the entire field visually illustrating particular seeds and/or populations of seeds to plant at different locations across the field and/or particular nutrient applications to apply at different locations across the field.