Nitrogen Estimation Using Selected Hyperspectral Bands
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Solution Overview
Problem
Current methods for determining nitrogen content in crops using remote sensing imagery are limited, as they often rely on single bands or vegetation indexes, failing to exploit the full potential of hyperspectral and multispectral data, and face challenges with large feature sets compared to the number of training samples.
Innovation Solution
Developing a system that uses machine learning and statistical techniques to create a mapping function between remote sensing imagery and nitrogen variables, employing dimensionality reduction, shrinkage-based methods, and vegetation indexes to select optimal features and improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral imagery with 128 bands is used to estimate nitrogen content, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the most relevant spectral bands from the full hyperspectral dataset. Specifically, it identifies and uses bands at 490nm, 530nm, 560nm, 680nm, and 730nm that are most sensitive to nitrogen content, rather than processing all 128 bands. This extraction approach maintains measurement precision while reducing computational complexity.
Solution Approach 2:
The patent segments the continuous hyperspectral data into discrete, strategically selected wavelength bands. By dividing the spectral range into specific bands that correspond to chlorophyll absorption features and other nitrogen-related spectral signatures, the system achieves accurate nitrogen estimation with fewer data points, thereby reducing device and processing complexity.
2Measurement precision
If extensive feature selection from hyperspectral data is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying and selecting the optimal spectral bands before actual nitrogen estimation. The five key bands (490nm, 530nm, 560nm, 680nm, 730nm) are predetermined based on their sensitivity to nitrogen content, allowing rapid estimation without time-consuming feature selection during field operations.
Solution Approach 2:
Instead of performing exhaustive feature selection on all 128 hyperspectral bands, the patent uses partial action by selecting only the essential five bands that provide sufficient information for accurate nitrogen estimation. This partial approach achieves the necessary measurement precision while dramatically reducing processing time.
3Measurement precision
If machine learning techniques with large feature sets are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential features needed for nitrogen estimation by selecting five specific spectral bands rather than using all available hyperspectral bands. This feature extraction simplifies the machine learning model input while maintaining prediction accuracy, thereby reducing model training complexity.
Solution Approach 2:
The patent changes the parameter dimensionality by transforming the high-dimensional hyperspectral data (128 bands) into a low-dimensional feature set (5 bands). This parameter reduction maintains the essential information for nitrogen estimation while significantly simplifying the machine learning model structure and training process.
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
The system effectively estimates nitrogen-related characteristics in crops by optimizing feature selection and model training, enhancing the accuracy and efficiency of nitrogen content determination in agricultural fields.
Implementation Method 1
a sensor that obtains remote sensing imagery data representing a plot of land containing crops
Data Source
AI summary
In an approach, hyperspectral and/or multispectral remote sensing images are automatically analyzed by a nitrogen analysis subsystem to estimate the value of nitrogen variables of crops or other plant life located within the images. For example, the nitrogen analysis subsystem may contain a data collector module, a function generator module, and a nitrogen estimator module. The data collector module prepares training data which is used by the function generator module to train a mapping function. The mapping function is then used by the nitrogen estimator module to estimate the values of nitrogen variables for a new remote sensing image that is not included in the training set. The values may then be reported and/or used to determine an optimal amount of fertilizer to add to a field of crops to promote plant growth.