Hyperspectral Soil Nitrogen Detection via Machine Learning
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Solution Overview
Problem
Current methods for determining nitrogen content in soil are time-consuming, laborious, and environmentally polluting, and fail to provide spatial distribution information, while visible-near infrared spectroscopy cannot visualize nitrogen distribution in soil profiles.
Innovation Solution
A machine learning-based hyperspectral detection and visualization method that samples soil, processes hyperspectral images, and establishes prediction models to accurately predict and visualize the spatial distribution of different nitrogen forms in soil profiles using algorithms like PLSR, ANN, and SVMR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional chemical analysis methods (Kjeldahl method) are used to determine nitrogen content, then measurement reliability is improved, but detection time and labor consumption increase significantly
Solution Approach 1:
The patent replaces conventional chemical analysis methods with hyperspectral imaging technology combined with machine learning algorithms. The imaging spectrometer captures spectral information non-destructively, and prediction models (PLSR, ANN, SVMR) rapidly determine nitrogen content without chemical reagents or lengthy processing, resolving the contradiction between measurement reliability and detection time.
2Measurement precision
If conventional chemical analysis methods are used, then nitrogen content can be determined, but spatial distribution information of soil nitrogen is lost
Solution Approach 1:
The patent transitions from point-based chemical analysis to area-based hyperspectral imaging. The imaging spectrometer captures spectral data across the entire soil profile cross-section, and the prediction models generate spatial distribution maps showing nitrogen content variations across different regions, thereby recovering spatial distribution information that was lost in conventional methods.
3Productivity
If visible-near infrared spectroscopy is used for rapid detection, then detection speed is improved, but the ability to visualize spatial distribution of nitrogen in soil profiles is lost
Solution Approach 1:
The patent merges hyperspectral imaging technology with machine learning prediction models. The imaging spectrometer rapidly captures spectral information across the visible-near infrared range (400-2500 nm), and the integrated prediction models simultaneously process this data to generate both quantitative nitrogen content measurements and spatial distribution visualizations, thereby preserving spatial information while maintaining rapid detection capability.
4Measurement precision
If conventional analysis methods are used, then nitrogen content can be measured, but environmental pollution and chemical reagent consumption occur
Solution Approach 1:
The patent substitutes chemical analysis methods with physical hyperspectral imaging and computational prediction. The imaging spectrometer measures spectral reflectance without contacting the soil with chemical reagents, and machine learning models predict nitrogen content from this physical measurement, completely eliminating the environmental pollution and reagent consumption associated with conventional chemical methods while maintaining 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 rapid and accurate prediction of total, alkali-hydrolyzable, ammonium, nitrate, and microbial biomass nitrogen content in soil profiles, providing detailed spatial distribution maps that overcome the limitations of conventional methods.
Implementation Method 1
In the spectral range from visible to near infrared (400-2500 nm), an imaging spectrometer is used to continuously image a target object, which has spectral information of different wavelengths of pixels in an image
Implementation Method 2
establishing a plurality of hyperspectral prediction models by using at least one learning algorithm; selecting an optimal prediction model corresponding to a soil nitrogen form from the plurality of hyperspectral prediction models based on evaluation indexes
Data Source
AI summary
The present disclosure provides a machine learning-based hyperspectral detection and visualization method of a nitrogen content in a soil profile. The method includes the following steps: collecting a plurality of soil profile samples; obtaining hyperspectral image data of a soil profile; selecting a plurality of rectangular ranges on a hyperspectral image as region of interest (ROIs), calculating an average spectral curve of all pixels in the ROIs, and analyzing and measuring standard contents of nitrogen in the soil samples corresponding to the ROIs; constructing hyperspectral prediction models of five types of soil nitrogen in the soil profile with reference to different learning algorithms respectively with an average spectrum of ROIs after preprocessing as a predictive variable and a standard soil nitrogen content as a response variable; selecting an optimal prediction model based on evaluation indexes to predict and visualize contents of different forms of nitrogen in the entire soil profile.