Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size
<p>Location of the study area and samples. The red rectangle in the map on the top right marks the boundary of the experimental field. The center-right map is a UAV multispectral image captured on May 5th. The photo on the bottom right shows the range of the sample.</p> "> Figure 2
<p>Methodological framework. A portion of the image material comes from Giannoni et al. (<a href="https://www.nireos.com" target="_blank">https://www.nireos.com</a>, accessed on 20 December 2018).</p> "> Figure 3
<p>The relief weights of the spectral indices in the severely and slightly diseased categories. The height of each bar is the actual weight of the corresponding index, and the accumulated weight is denoted by the top of the bar.</p> "> Figure 4
<p>Kendall results among the spectral indices and between each spectral index and disease rate (the last line). The middle part is the Kendall correlation coefficient between the spectral indices of the corresponding row and column. The Kendall and T-test results for the disease rate and each spectral index in the corresponding column are shown in the last row, in which “*” corresponds to the 90% confidence interval and “**” corresponds to the 95% confidence interval.</p> "> Figure 5
<p>Kendall correlation coefficients between the PC1s of texture features and the disease rates on May 3 (<b>a</b>) and May 8 (<b>b</b>).</p> "> Figure 6
<p>The average values and a box map of the model accuracies. Obtained utilizing only spectral features (the leftmost) and combined spectral features and texture features in windows of different sizes (right), on May 3 (<b>a</b>) and May 8 (<b>b</b>).</p> "> Figure 7
<p>Severely diseased area detection performed by using selected spectral features and the texture features in the optimal window size. (<b>a</b>) The results on May 3, when the optimal window size was 5 × 5 pixels; (<b>b</b>) the results on May 8, when the optimal window size was 17 × 17 pixels.</p> "> Figure 8
<p>Detection results on May 3 (<b>a</b>) window size = 5 × 5, (<b>b</b>) window size = 9 × 9, (<b>c</b>) window size = 13 × 13, (<b>d</b>) window size = 17 × 17, (<b>e</b>) window size = 21 × 21, and (<b>f</b>) window size = 25 × 25.</p> "> Figure 8 Cont.
<p>Detection results on May 3 (<b>a</b>) window size = 5 × 5, (<b>b</b>) window size = 9 × 9, (<b>c</b>) window size = 13 × 13, (<b>d</b>) window size = 17 × 17, (<b>e</b>) window size = 21 × 21, and (<b>f</b>) window size = 25 × 25.</p> "> Figure 9
<p>Detection results on May 8 (<b>a</b>) window size = 5 × 5, (<b>b</b>) window size = 9 × 9, (<b>c</b>) window size = 13 × 13, (<b>d</b>) window size = 17 × 17, (<b>e</b>) window size = 21 × 21, and (<b>f</b>) window size = 25 × 25.</p> "> Figure 10
<p>The average ROCs of the logistic models on May 3. (<b>a</b>–<b>f</b>) The average ROCs of models with spectral features and texture features extracted from window sizes of 5 × 5, 9 × 9, 13 × 13, 17 × 17, 21 × 21, and 25 × 25 pixels.</p> "> Figure 10 Cont.
<p>The average ROCs of the logistic models on May 3. (<b>a</b>–<b>f</b>) The average ROCs of models with spectral features and texture features extracted from window sizes of 5 × 5, 9 × 9, 13 × 13, 17 × 17, 21 × 21, and 25 × 25 pixels.</p> "> Figure 11
<p>The average ROCs of logistic models on May 8. (<b>a</b>–<b>f</b>) The average ROCs of models with spectral features and texture features extracted from window sizes of 5 × 5, 9 × 9, 13 × 13, 17 × 17, 21 × 21, and 25 × 25 pixels.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Field Investigation
2.2. Optimal Window Selection of Texture Features for Wheat FHB Detection
2.2.1. Spectral and Texture Feature Extraction and Selection
2.2.2. Optimal Window Size Selection and Model Performance Analysis
3. Results
3.1. Sensitive Spectral and Texture Features Selection
3.2. FHB Detection with a Logistic Model and Its Performance Analysis
3.2.1. FHB Detection with the Logistic Model Using Texture Features in Optimal Window Sizes and Spectral Features
3.2.2. Comparison of Model Performance and Results among Texture Features in Different Window Sizes
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Predicted Class | |||
---|---|---|---|
Severely Diseased | Slightly Diseased | ||
Actual Class | Severely Diseased | True Positive (TP) | False Negative (FN) |
Slightly Diseased | False Positive (FP) | True Negative (TN) |
Spectral Features | Formulation | Correlation |
---|---|---|
PSRI | Plant stress | |
ARI | Anthocyanin | |
Red-edge position | Pigment |
Date | Index Type | Window Size | |||||
---|---|---|---|---|---|---|---|
5 | 9 | 13 | 17 | 21 | 25 | ||
May 3rd | OA | 0.90 | 0.90 | 0.81 | 0.73 | 0.73 | 0.63 |
F1 | 0.79 | 0.74 | 0.70 | 0.70 | 0.65 | 0.55 | |
AA_5 | 0.804 | 0.789 | 0.730 | 0.723 | 0.699 | 0.679 | |
May 8th | OA | 0.81 | 0.73 | 0.90 | 0.90 | 0.90 | 0.73 |
F1 | 0.74 | 0.70 | 0.79 | 0.83 | 0.79 | 0.70 | |
AA_5 | 0.774 | 0.767 | 0.806 | 0.823 | 0.783 | 0.745 |
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Xiao, Y.; Dong, Y.; Huang, W.; Liu, L.; Ma, H. Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. Remote Sens. 2021, 13, 2437. https://doi.org/10.3390/rs13132437
Xiao Y, Dong Y, Huang W, Liu L, Ma H. Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. Remote Sensing. 2021; 13(13):2437. https://doi.org/10.3390/rs13132437
Chicago/Turabian StyleXiao, Yingxin, Yingying Dong, Wenjiang Huang, Linyi Liu, and Huiqin Ma. 2021. "Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size" Remote Sensing 13, no. 13: 2437. https://doi.org/10.3390/rs13132437
APA StyleXiao, Y., Dong, Y., Huang, W., Liu, L., & Ma, H. (2021). Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. Remote Sensing, 13(13), 2437. https://doi.org/10.3390/rs13132437