Estimation of Total Nitrogen Content in Topsoil Based on Machine and Deep Learning Using Hyperspectral Imaging
Abstract
:1. Introduction
2. Materials and Methods
2.1. Topsoil Sample Collection and Preparation
2.2. Hyperspectral Imaging System
2.3. Collection of Hyperspectral Image (HSI)
2.4. Spectral Preprocessing
2.5. Development of Total Nitrogen (TN) Content Prediction Models
2.5.1. Partial Least Squares Regression (PLSR)
2.5.2. One-Dimensional Convolutional Neural Networks (1D-CNNs)
2.5.3. Model Performance Evaluation
3. Results
3.1. TN Content of Topsoil Samples
3.2. Topsoil Spectral Characteristics
3.3. TN Content Prediction Model
3.3.1. Performance of the PLSR Model
3.3.2. Performance of the 1D-CNN Model
3.4. Regression Coefficient of the PLSR Model
3.5. Performance Evaluation of the Optimal Model for Predicting TN Content in Topsoil
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Preprocessing | Gap Size | Characteristic [35] | ||
---|---|---|---|---|
VNIR (1) (nm) | NIR (2) (nm) | |||
Smoothing (Moving average) | 6 | 7.23 | remove the high-frequency noise from samples | |
Savitzky–Golay first-order derivatives | 10 | 16.87 | eliminate baseline drift and highlight certain peaks in the spectral information | |
14 | 21.69 | |||
18 | 26.51 | |||
Savitzky–Golay second-order derivatives | 10 | 16.87 | eliminate drift of the baseline and identify inflection points in the spectrum | |
14 | 21.69 | |||
18 | 26.51 | |||
Normalization | Maximum | - | - | scales all spectral data to a value between 0 and 1 and minimizes errors presented due to the sample preparation step |
Range | - | - | ||
Mean | - | - | ||
SNV | - | - | eliminate the effects of the instrument’s optical path | |
MSC | - | - | alleviate problems arising from scattered light |
Index | Total | HR (1) | NR (2) | SR (3) | GR (4) |
---|---|---|---|---|---|
Calibration dataset | 834 | 300 | 180 | 174 | 180 |
Prediction dataset | 417 | 150 | 90 | 87 | 90 |
Layer | Filters | Filter Size | Padding | Stride | Activation |
---|---|---|---|---|---|
Convolutional (Conv1) | 64 | 5 | same | - | ReLU (1) |
Max-pooling (Max pooling 1) | - | 3 | - | 2 | - |
Convolutional (Conv2) | 64 | 5 | same | - | ReLU |
Max-pooling (Max pooling 2) | - | 3 | - | 2 | - |
Convolutional (Conv3) | 64 | 5 | same | - | ReLU |
Max-pooling (Max pooling 3) | - | 3 | - | 2 | - |
Flatten | - | - | - | - | - |
Dropout (0.5) | - | - | - | - | - |
Fully connected (Dense1) | 10 | - | - | - | ReLU |
Fully connected (Dense2) | 1 | - | - | - | - |
Wavelength | Pixel Size (Pixel) | Dataset | Total | HR (1) | NR (2) | SR (3) | GR (4) |
---|---|---|---|---|---|---|---|
VNIR | 600 | Calibration | 834 | 300 | 180 | 174 | 180 |
Prediction | 417 | 150 | 90 | 87 | 90 | ||
75 150 300 | Calibration | 8340 | 3000 | 1800 | 1740 | 1800 | |
Prediction | 4170 | 1500 | 900 | 870 | 900 | ||
NIR | 1800 | Calibration | 834 | 300 | 180 | 174 | 180 |
Prediction | 417 | 150 | 90 | 87 | 90 | ||
225 450 900 | Calibration | 8340 | 3000 | 1800 | 1740 | 1800 | |
Prediction | 4170 | 1500 | 900 | 870 | 900 |
Topsoil Sample | Count | TN Content (g kg−1) | |||
---|---|---|---|---|---|
Min. | Max. | Ave. | Std. | ||
Total | 139 | 0.07 | 6.54 | 1.33 | 0.87 |
HR (1) | 50 | 0.13 | 2.64 | 1.32 | 0.57 |
NR (2) | 30 | 0.07 | 2.34 | 1.09 | 0.51 |
SR (3) | 29 | 0.21 | 4.68 | 1.72 | 1.10 |
GR (4) | 30 | 0.10 | 6.54 | 1.22 | 1.18 |
Topsoil Sample | Wavelength Range (nm) | Preprocessing | Rc2 | RMSEC (g kg−1) | Rv2 | RMSEV (g kg−1) | Factor |
---|---|---|---|---|---|---|---|
Total | 400–1000 | Mean Normalization | 0.668 | 0.50 | 0.658 | 0.51 | 4 |
895–1720 | Maximum Normalization | 0.761 | 0.42 | 0.736 | 0.45 | 13 | |
HR (1) | 400–1000 | Moving average | 0.649 | 0.33 | 0.590 | 0.36 | 7 |
895–1720 | Moving average | 0.825 | 0.24 | 0.778 | 0.27 | 14 | |
NR (2) | 400–1000 | Mean Normalization | 0.895 | 0.16 | 0.765 | 0.25 | 10 |
895–1720 | Maximum Normalization | 0.884 | 0.17 | 0.766 | 0.25 | 16 | |
SR (3) | 400–1000 | Moving average | 0.882 | 0.37 | 0.837 | 0.44 | 7 |
895–1720 | Maximum Normalization | 0.965 | 0.20 | 0.933 | 0.28 | 14 | |
GR (4) | 400–1000 | Mean Normalization | 0.925 | 0.32 | 0.916 | 0.34 | 4 |
895–1720 | Maximum Normalization | 0.911 | 0.35 | 0.871 | 0.42 | 13 |
VNIR (400–1000 nm) | Average (600 pixels) | 75 pixels | 150 pixels | 300 pixels | |
Rv2 | 0.684 | 0.759 | 0.955 | 0.903 | |
RMSEV (g kg−1) | 0.55 | 0.42 | 0.18 | 0.27 | |
Rp2 | 0.651 | 0.731 | 0.844 | 0.839 | |
RMSEP (g kg−1) | 0.51 | 0.45 | 0.34 | 0.35 | |
NIR (895–1720 nm) | Average (1800 pixels) | 225 pixels | 450 pixels | 900 pixels | |
Rv2 | 0.360 | 0.760 | 0.887 | 0.931 | |
RMSEV (g kg−1) | 0.78 | 0.42 | 0.29 | 0.23 | |
Rp2 | 0.334 | 0.730 | 0.816 | 0.793 | |
RMSEP (g kg−1) | 0.71 | 0.45 | 0.37 | 0.39 |
Wavelength Range (nm) | Preprocessing | Rv2 | RMSEV (g kg−1) | Rp2 | RMSEP (g kg−1) | |
---|---|---|---|---|---|---|
Total | 400–1000 | 1st derivative (10 nm) | 0.954 | 0.19 | 0.852 | 0.33 |
895–1720 | SNV | 0.982 | 0.11 | 0.922 | 0.24 | |
HR (1) | 400–1000 | 1st derivative (10 nm) | 0.962 | 0.11 | 0.822 | 0.24 |
895–1720 | SNV | 0.978 | 0.08 | 0.877 | 0.20 | |
NR (2) | 400–1000 | 1st derivative (10 nm) | 0.972 | 0.09 | 0.921 | 0.14 |
895–1720 | Moving average | 0.972 | 0.09 | 0.844 | 0.20 | |
SR (3) | 400–1000 | RAW | 0.966 | 0.29 | 0.928 | 0.29 |
895–1720 | 1st derivative (16.87 nm) | 0.986 | 0.14 | 0.947 | 0.25 | |
GR (4) | 400–1000 | SNV | 0.991 | 0.11 | 0.982 | 0.16 |
895–1720 | 1st derivative (12.05 nm) | 0.994 | 0.09 | 0.991 | 0.11 |
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Kim, M.-J.; Lee, J.-E.; Back, I.; Lim, K.J.; Mo, C. Estimation of Total Nitrogen Content in Topsoil Based on Machine and Deep Learning Using Hyperspectral Imaging. Agriculture 2023, 13, 1975. https://doi.org/10.3390/agriculture13101975
Kim M-J, Lee J-E, Back I, Lim KJ, Mo C. Estimation of Total Nitrogen Content in Topsoil Based on Machine and Deep Learning Using Hyperspectral Imaging. Agriculture. 2023; 13(10):1975. https://doi.org/10.3390/agriculture13101975
Chicago/Turabian StyleKim, Min-Jee, Jae-Eun Lee, Insuck Back, Kyoung Jae Lim, and Changyeun Mo. 2023. "Estimation of Total Nitrogen Content in Topsoil Based on Machine and Deep Learning Using Hyperspectral Imaging" Agriculture 13, no. 10: 1975. https://doi.org/10.3390/agriculture13101975