Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content
<p>(<b>a</b>) Study area. (<b>b</b>) Location of sampled trees.</p> "> Figure 2
<p>Flow chart for quantitative assessment of apple mosaic disease severity based on hyperspectral images.</p> "> Figure 3
<p>(<b>a</b>) Original spectral reflectance, and (<b>b</b>) Savitzky–Golay filtered spectral reflectance.</p> "> Figure 4
<p>Flow chart of Stacked–Boosting ensemble learning model.</p> "> Figure 5
<p>(<b>a</b>) Spectral reflectance and (<b>b</b>) SI of leaves with different LCC.</p> "> Figure 6
<p>CARS results. (<b>a</b>) Variation in RMSECV; (<b>b</b>) variation in the number of selected features; (<b>c</b>) variation in the trend of regression coefficients; (<b>d</b>) selected wavelengths.</p> "> Figure 7
<p>(<b>a</b>) Leaf RGB image and (<b>b</b>) LCC distribution with average LCC.</p> "> Figure 8
<p>Correlation of (<b>a</b>) average LCC and (<b>b</b>) CV of LCC with disease spot area.</p> "> Figure 9
<p>Confusion matrix of the classification results.</p> "> Figure 10
<p>Prediction results of (<b>a</b>) Random Forest; (<b>b</b>) XGBoost; (<b>c</b>) Stacked–Boosting.</p> "> Figure 11
<p>Feature importance.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Leaf Sample Collection
2.2. Data Acquisition
2.2.1. LCC Determination
2.2.2. Hyperspectral Image Acquisition
2.3. Data Processing
2.3.1. Spectral Data Pre-Processing
2.3.2. Sample Split
2.3.3. Feature Selection Method
2.3.4. Spectral Sensitivity Index
2.3.5. Coefficient of Variation
2.4. Modeling Method
2.4.1. Basic Models
2.4.2. Stacked–Boosting for Predictive Models
2.4.3. Model Evaluation Methodology
3. Results
3.1. Spectral Characteristics of Leaves
3.2. Characteristic Wavelength Extraction
3.3. Modeling Evaluation of LCC Prediction
3.4. Inversion of LCC by HSI
3.5. Relationship between LCC Statistics and Percentage of Disease Spot Area
3.6. Identify Disease Severity Based on Average LCC and Sensitive Wavelengths
4. Discussion
4.1. Stacked–Boosting Modeling Summary
4.2. Quantitative Description of Disease Severity Using Chlorophyll Content
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Disease Severity | Percentage of Disease Spot Area | Number of Measurements | Measurement Area |
---|---|---|---|
health | 0% | 2 | Two random uninfected areas |
slight | 0~25% | 3 | Two random uninfected areas and one infected area |
moderate | 25~50% | 3 | One random uninfected area and two infected areas |
severe | >50% | 2 | Two random infected areas |
Sample | Number of Samples | Minimum (μg/cm2) | Maximum (μg/cm2) | Mean (μg/cm2) | Standard Deviation |
---|---|---|---|---|---|
Calibration set | 270 | 4.14 | 55.60 | 28.93 | 13.27 |
Validation set | 90 | 6.30 | 53.62 | 35.42 | 13.21 |
Total | 360 | 4.14 | 55.60 | 30.55 | 13.53 |
Models | Hyperparameters and the Search Range |
---|---|
CART | max_depth: (2~20) |
EN | alpha: (0.01~10), L1_ratio: (0~1) |
GPR | alpha: (1 × 10−10), n_restarts_optimizer: (1~50) |
KNN | weight: distance, n_neighbors: (1~10), p: (1~10) |
KRR | kernel: laplacian, alpha: (0.01~1) |
MLP | solver: lbfgs, hidden_layer_sizes: (0~100,0~100), learning_rate: (0.01~1) |
SVR | kernel: rbf, C: (1~10), gamma: (0.5~5) |
AdaBoost | base_estimator: (CART, EN, GPR, KRR, MLP, SVR), n_estimators: (1~100), learning_rate: (0.01~1) |
CatBoost | task_type: GPU, iterations: (10~500), depth: (2~10), learning_rate: (0.01~1), L2_leaf_reg: (1~50) |
Model | |||||
---|---|---|---|---|---|
CART | 3.8288 | 0.9164 | 4.6980 | 0.8722 | 2.6818 |
EN | 3.0413 | 0.9473 | 3.4346 | 0.9317 | 3.3022 |
GPR | 2.1294 | 0.9741 | 3.8393 | 0.9146 | 3.3059 |
KNN | 0.0000 | 1.0000 | 3.3096 | 0.9367 | 3.9322 |
KRR | 2.1399 | 0.9739 | 3.0436 | 0.9463 | 4.0729 |
MLP | 3.0368 | 0.9474 | 3.2271 | 0.9397 | 4.1084 |
SVR | 2.7234 | 0.9577 | 3.4373 | 0.9316 | 3.4026 |
CART-Boosting | 2.4479 | 0.9658 | 2.7598 | 0.9559 | 4.7031 |
EN-Boosting | 3.0095 | 0.9484 | 3.3658 | 0.9344 | 3.3512 |
GPR-Boosting | 1.4083 | 0.9887 | 3.1167 | 0.9437 | 4.1014 |
KNN-Boosting | 0.0493 | 1.0000 | 3.0414 | 0.9404 | 4.3078 |
KRR-Boosting | 2.0279 | 0.9765 | 2.9451 | 0.9498 | 4.2451 |
MLP-Boosting | 2.5351 | 0.9634 | 2.7623 | 0.9558 | 4.7044 |
SVR-Boosting | 2.6465 | 0.9601 | 3.3853 | 0.9336 | 3.4364 |
Stacked-Boosting | 1.3608 | 0.9894 | 2.4796 | 0.9644 | 5.1054 |
Feature | ||||
---|---|---|---|---|
500.02 nm | 97.04 | 0.9604 | 74.44 | 0.6573 |
550.95 nm | 93.70 | 0.9161 | 86.67 | 0.8188 |
602.36 nm | 96.67 | 0.9556 | 85.56 | 0.8045 |
649.05 nm | 97.04 | 0.9604 | 78.89 | 0.7150 |
680.39 nm | 93.70 | 0.9159 | 66.67 | 0.5550 |
722.44 nm | 90.37 | 0.8713 | 57.78 | 0.4441 |
Average LCC | 91.85 | 0.8913 | 91.11 | 0.8811 |
CV of LCC | 93.33 | 0.9111 | 81.11 | 0.7468 |
all sensitive wavelengths | 97.41 | 0.9654 | 92.22 | 0.8960 |
all LCC statistics | 97.78 | 0.9704 | 95.56 | 0.9406 |
sensitive wavelengths + LCC statistics | 99.26 | 0.9901 | 98.89 | 0.9852 |
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Liu, Y.; Zhang, Y.; Jiang, D.; Zhang, Z.; Chang, Q. Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content. Remote Sens. 2023, 15, 2202. https://doi.org/10.3390/rs15082202
Liu Y, Zhang Y, Jiang D, Zhang Z, Chang Q. Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content. Remote Sensing. 2023; 15(8):2202. https://doi.org/10.3390/rs15082202
Chicago/Turabian StyleLiu, Yanfu, Yu Zhang, Danyao Jiang, Zijuan Zhang, and Qingrui Chang. 2023. "Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content" Remote Sensing 15, no. 8: 2202. https://doi.org/10.3390/rs15082202
APA StyleLiu, Y., Zhang, Y., Jiang, D., Zhang, Z., & Chang, Q. (2023). Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content. Remote Sensing, 15(8), 2202. https://doi.org/10.3390/rs15082202