Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Processing
2.2.1. Anth Quantification
2.2.2. Hyperspectral Data Acquisition
2.3. Analytical Methods
2.3.1. Definition of Sensitivity Index (SI)
2.3.2. Construction of Vegetation Indices with Two Arbitrary Bands
2.3.3. Linear Discriminant Analysis (LDA) Classification Model
2.3.4. Support Vector Machine (SVM) Classification Model
2.3.5. Regression Models
2.4. Evaluation of Precision
2.5. Analytical Framework
3. Results
3.1. Leaf Anth Statistics
3.2. Characteristics of Reflectance Spectra
3.3. Correlation between Anth and Spectral Reflectance
3.4. Vegetation Indices
3.4.1. Various Vegetation Indices
3.4.2. VIc Based on Two Arbitrary Bands
3.5. MDMV Identification
3.6. Classic Regression Analysis Based on a Sensitive Band
3.7. Anth Regression Models Based on VIs, VIc, and VIs + VIc + Rλ
4. Discussion
4.1. Link between Spectral Reflectance and Plant Disease
4.2. Application of Vegetation Indices Based on Two Arbitrary Bands
4.3. RS-Based Identification of MDMV-Infected Leaves
4.4. Application of Machine Learning Algorithms in Precision Agriculture
5. Conclusions
- (1)
- The spectral differences between red and healthy leaves were mainly concentrated in the 493–764 nm region, and the maximum difference was recorded near 700 nm.
- (2)
- The red leaf spectrum showed bimodal characteristics in the visible range. With the aggravation of the disease (i.e., increase in Anth), the reflectance of the left peak of the spectrum (550 nm) gradually decreased until it disappeared. Simultaneously, the reflectance of the right peak increased gradually, and the absorption characteristics near 680 nm disappeared. With worsening MDMV infection, the position of the red edge of the reflectance spectrum appeared as a “blue shift.”
- (3)
- The LAD and SVM models constructed based on VIc performed better in recognizing MDMV, with the classification accuracy of 100%, followed by the models based on VIs; the models based on Rλ showed the poorest classification accuracy.
- (4)
- The MLR model based on Rλ + VIs + VIc (R2c = 0.85, R2v = 0.74) was the best for monitoring the severity of MDMV infection, while the SVMR model based on Rλ + VIs + VIc (R2c = 0.68, R2v = 0.66) was the best for the estimation of Anth in healthy maize leaves.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | N | Min (μg·cm−2) | Max (μg·cm−2) | Mean (μg·cm−2) | SD | Variance | CV (%) |
---|---|---|---|---|---|---|---|
healthy | 360 | 0.03 | 0.11 | 0.06 | 0.02 | 3.24 × 10−4 | 29.36 |
red | 72 | 0.04 | 0.76 | 0.19 | 0.18 | 0.03 | 94.74 |
VIs | Equations | Bands | References | VIs | Equations | Band | References |
---|---|---|---|---|---|---|---|
SR | R800/R680 | 2 | [42] | SIPI | (R680 − R500)/R750 | 3 | [43] |
NDVI | (R800 − R670)/(R800 + R670) | 2 | [44] | GNDVI | (R800 − R550)/(R800 + R550) | 2 | [45] |
TVI | 0.5(120(R750 − R550) − 200(R670 − R550)) | 3 | [46] | MCARI | R700 ((R700 − R670) − 0.2(R700 − R550))/R670 | 3 | [47] |
CHLI | R700/(R700 + R710 − 1) | 2 | [48] | MCARI1 | 1.2(2.5(R800 − R670) − 1.3(R800 − R550)) | 3 | [49] |
VRI | R740/R720 | 2 | [50] | PSRI | (R680 − R500)/R750 | 3 | [51] |
PRI | (R531 − R570)/(R531 + R570) | 2 | [52] | RNDVI | (R750 − R705)/(R750 + R705) | 2 | [53] |
Models | Parameters | Calibration Set (nr = 48, nh = 240) | Validation Set (nr = 24, nh = 120) | ||||
---|---|---|---|---|---|---|---|
Red | Healthy | Accuracy/% | Red | Healthy | Accuracy/% | ||
LDA | Rλ | 6 | 240 | 56.3 | 6 | 120 | 62.5 |
VIs | 34 | 240 | 85.4 | 12 | 120 | 75.0 | |
VIc | 48 | 240 | 100.0 | 24 | 120 | 100.0 | |
SVM | Rλ | 0 | 240 | 50.0 | 0 | 120 | 50.0 |
VIs | 36 | 240 | 87.5 | 16 | 120 | 83.3 | |
VIc | 48 | 240 | 100.0 | 24 | 120 | 100.0 |
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Luo, L.; Chang, Q.; Wang, Q.; Huang, Y. Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements. Remote Sens. 2021, 13, 4560. https://doi.org/10.3390/rs13224560
Luo L, Chang Q, Wang Q, Huang Y. Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements. Remote Sensing. 2021; 13(22):4560. https://doi.org/10.3390/rs13224560
Chicago/Turabian StyleLuo, Lili, Qingrui Chang, Qi Wang, and Yong Huang. 2021. "Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements" Remote Sensing 13, no. 22: 4560. https://doi.org/10.3390/rs13224560