Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value
"> Figure 1
<p>Pepper plant leaf position.</p> "> Figure 2
<p>Sampling area of pepper leaves.</p> "> Figure 3
<p>Schematic diagram of the GaiaSorter hyperspectral imaging system. 1. Hyperspectral imager, 2. imaging lens, 3. halogen lamp, 4. sample table, 5. correction whiteboard, and 6. electric translation table.</p> "> Figure 4
<p>Original spectral curve.</p> "> Figure 5
<p>Technical Roadmap.</p> "> Figure 6
<p>Correlation of SPAD values and spectral reflectance.</p> "> Figure 7
<p>Characteristic variable selection process of sCARS algorithm. (<b>a</b>) Changes in the number of waveband variables. (<b>b</b>) Validation of RMSECV. (<b>c</b>) Path of variable regression coefficients.</p> "> Figure 8
<p>IRIV algorithm selection process: (<b>a</b>) The change in the number of retained informative variables in each round; (<b>b</b>) Changes in P value and DMEAN in the sixth round.</p> "> Figure 9
<p>Comparison chart of optimal variable distribution.</p> "> Figure 10
<p>Scatter plot of measured and predicted values of the four models: (<b>a</b>) PLSR; (<b>b</b>) XGBoost; (<b>c</b>) RFR; and (<b>d</b>) GBDT.</p> "> Figure 11
<p>Distribution of SPAD value in the lower leaf in different models: (<b>a</b>,<b>e</b>—PLSR), (<b>b</b>,<b>f</b>—XGBoost), (<b>c</b>,<b>g</b>—RFR), (<b>d</b>,<b>h</b>—GBDT).</p> "> Figure 12
<p>Distribution of SPAD value in the middle leaf in different models: (<b>a</b>,<b>e</b>—PLSR), (<b>b</b>,<b>f</b>—XGBoost), (<b>c</b>,<b>g</b>—RFR), (<b>d</b>,<b>h</b>—GBDT).</p> "> Figure 13
<p>Distribution of SPAD value in the upper leaf in different models: (<b>a</b>,<b>e</b>—PLSR), (<b>b</b>,<b>f</b>—XGBoost), (<b>c</b>,<b>g</b>—RFR), (<b>d</b>,<b>h</b>—GBDT).</p> "> Figure 14
<p>Predicted SPAD value and measured value with the standard deviation as error bars (No.1,2—<a href="#sensors-22-00183-f011" class="html-fig">Figure 11</a>b,f, No.3,4—<a href="#sensors-22-00183-f012" class="html-fig">Figure 12</a>b,f, No.5,6—<a href="#sensors-22-00183-f013" class="html-fig">Figure 13</a>b,f).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Collection
2.2. Chlorophyll Determination
2.3. Hyperspectral Data Collection
2.4. Spectral Extraction
2.5. Research Methods
2.5.1. Correlation Coefficient Method
2.5.2. Stability Competitive Adaptive Reweighted Sampling (sCARS)
- Select N wavelength subsets from N Monte Carlo sampling [19] runs in an iterative and competitive manner. In each sampling process, a fixed proportion of samples is randomly selected to establish a calibration model.
- Perform a two-step process to select characteristic wavelengths: use an exponential decrease function [17] for wavelength selection and use adaptive reweighted sampling to achieve competitive wavelength selection.
- Use cross-validation [20] to select the subset with the smallest cross-validation root mean square error (RMSECV).
2.5.3. Iteratively Retaining Informative Variables
2.5.4. Partial Least-Squares Regression
2.5.5. Extreme Gradient Boosting (XGBoost)
2.5.6. Random Forest Regression (RFR)
2.5.7. Gradient Boosting Decision Tree (GBDT)
2.6. Accuracy Evaluation
2.7. Technical Roadmap
3. Results
3.1. Selection of Characteristic Band Based on CA Algorithm
3.2. Selection of Characteristic Band Based on SCARS Algorithm
3.3. Selection of Characteristic Band Based on IRIV Algorithm
3.4. Screening Results
3.5. Optimal Algorithm Selection
3.5.1. Accuracy Comparison of Different Methods
3.5.2. Model Construction Based on the Bands selected by the IRIV Algorithm
3.6. Chlorophyll Distribution of Pepper Leaves
3.7. Statistical Summary Based on the IRIV-XGBoost Algorithm
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Wavelength Variable Type | Classification Rules |
---|---|
Strongly informative | |
Weakly informative | |
Uninformative | |
Interfering |
Selection Method | Number of Bands | Modeling Algorithm | |||
---|---|---|---|---|---|
Full bands | 176 | PLSR | 0.52 | 2.57 | 2.11 |
XGBoost | 0.48 | 2.80 | 2.28 | ||
RFR | 0.42 | 2.95 | 2.83 | ||
GBDT | 0.50 | 2.76 | 2.19 | ||
CA | 76 | PLSR | 0.48 | 2.59 | 2.1 |
XGBoost | 0.29 | 3.00 | 2.39 | ||
RFR | 0.41 | 2.95 | 2.4 | ||
GBDT | 0.44 | 2.84 | 2.23 | ||
sCARS | 46 | PLSR | 0.55 | 2.59 | 2.13 |
XGBoost | 0.54 | 2.68 | 2.17 | ||
RFR | 0.43 | 2.92 | 2.32 | ||
GBDT | 0.53 | 2.74 | 2.17 | ||
IRIV | 26 | PLSR | 0.84 | 2.46 | 2.02 |
XGBoost | 0.81 | 2.76 | 2.30 | ||
RFR | 0.80 | 2.85 | 2.28 | ||
GBDT | 0.80 | 2.82 | 2.22 |
Leaf Position | Measured Value | Model Method | Min Value | Max Value |
---|---|---|---|---|
Lower leaf | 66.0 | PLSR | 19 | 82 |
XGBoost | 43 | 69 | ||
RFR | 46 | 67 | ||
GBDT | 43 | 70 | ||
69.0 | PLSR | 21 | 85 | |
XGBoost | 44 | 69 | ||
RFR | 47 | 67 | ||
GBDT | 45 | 69 | ||
Middle leaf | 61.0 | PLSR | 14 | 82 |
XGBoost | 42 | 69 | ||
RFR | 45 | 66 | ||
GBDT | 42 | 69 | ||
60.6 | PLSR | 12 | 83 | |
XGBoost | 41 | 69 | ||
RFR | 45 | 66 | ||
GBDT | 44 | 69 | ||
Upper leaf | 48.3 | PLSR | 3 | 73 |
XGBoost | 42 | 67 | ||
RFR | 45 | 65 | ||
GBDT | 42 | 68 | ||
50.5 | PLSR | 2 | 72 | |
XGBoost | 42 | 67 | ||
RFR | 45 | 64 | ||
GBDT | 42 | 68 |
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Yuan, Z.; Ye, Y.; Wei, L.; Yang, X.; Huang, C. Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value. Sensors 2022, 22, 183. https://doi.org/10.3390/s22010183
Yuan Z, Ye Y, Wei L, Yang X, Huang C. Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value. Sensors. 2022; 22(1):183. https://doi.org/10.3390/s22010183
Chicago/Turabian StyleYuan, Ziran, Yin Ye, Lifei Wei, Xin Yang, and Can Huang. 2022. "Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value" Sensors 22, no. 1: 183. https://doi.org/10.3390/s22010183
APA StyleYuan, Z., Ye, Y., Wei, L., Yang, X., & Huang, C. (2022). Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value. Sensors, 22(1), 183. https://doi.org/10.3390/s22010183