It is highly important to accurately monitor wheat scab and provide technical guidance for the crop pests and diseases. In this study, relevant analysis was performed among spectral reflectance, first-derivate data, and the disease severity data through ASD hyperspectral data. Two sensitive spectral wavelength ranges of 450–488 nm and 500–540 nm were selected. Then, a new wheat scab index (WSI) consisting of the two bands was proposed. The inversion models of the scab severities were comparatively built by unitary linear regression and multiple stepwise regression techniques. The results showed that the WSI had a significant linear relationship with severity of disease compared with other commonly used spectral indices. The fitting
China is a big agricultural country with vast land and abundant agricultural resources. Wheat is the second largest food crop in China, and its planting area ranks second. During the growth period of wheat, the occurrence of some pests and diseases will seriously affect the yield and quality. As one of the seriously infected diseases, wheat scab caused by a variety of
Studies have shown that [
The spectral index can realize the identification and monitoring of different diseases by filtering and combining original bands. Bravo et al. [
Although many studies have been conducted on wheat disease, there is little research on scab. Most of the scholars used hyperspectral data to study stripe rust, powdery mildew, and aphid on leaf or canopy scale. Scab is a typical ear disease, so a proprietary index that can be used to monitor wheat ear-scale scab is desirable. Therefore, based on previous studies, our study aimed (1) to identify wavebands that are sensitive to wheat scab at ear scale; (2) to construct a new spectral index (WSI) for characterizing the spectral changes caused by scab infestation; and (3) to evaluate the performance of the proposed WSI for retrieving scab severities using linear regression method.
The experiment was carried out at a test field in Guohe Town, Lujiang County, Anhui Province (31°25.6′N, 117°9.2′E), on May 8, 2018 (Figure
Location of the study site.
The spectral reflectance of the wheat ear was collected with an ASD FieldSpec Pro spectrometer (350–2500 nm) with the spectral resolution of 3 nm during the 350–1000 nm and 10 nm within the 1000–2500 nm range. Measurements were taken at sunny noon time (10 : 00–14 : 00). During the measurement, every wheat ear was placed in the middle of the black cloth, and the probe of the sensor was held vertically downward to measure the upright, front, and side data. A 40 cm × 40 cm BaSO4 calibration panel was measured to correct the reflectance. The spectra in different directions for each sample were measured 20 times, and then the mean was used as the reflectance. Hyperspectral data for the front, side, and upright sides of each wheat ear were finally determined. The calculation formula is as follows:
The disease severity is defined using the proportion of the infected spikelets to the total number. According to the rules for monitoring and forecast of wheat head blight (GB/T15796-2011), it is divided into 5 levels: Level 0 (0), Level 1 (0–1/4), Level 2 (1/4–1/2), Level 3 (1/2–3/4), and Level 4 (3/4–1).
In the experimental field, 41 sample plots were selected and an infected wheat was randomly selected as our research sample from each sample plot. A total of 41 infected wheat samples were obtained with different severity levels from the field survey (Figure
Spectral reflectance curves of 41 wheat ear samples.
In order to analyze the spectral response characteristics of wheat scab, we averaged the sample spectral reflectance of each level. A correlation analysis was performed between spectral reflectance and disease severity was used to pick up the sensitive positions and ranges. However, due to the influence of noise, we failed to achieve good results through the original spectral reflectance. The spectral differential was used to reduce external influences, and its formula is as follows [
Leave-one-out cross validation was applied to verify the classification accuracy. Its main idea [
Spectral indices are widely used for monitoring, analyzing, and mapping temporal and spatial variation in vegetation [
Definitions of vegetation indices and spectral differential characteristics.
Vegetation indices/spectral differential characteristics | Calculation formulas | References |
---|---|---|
NDVI | ( |
[ |
RVSI | [( |
[ |
TVI | 0.5[120( |
[ |
MCARI | [( |
[ |
TCARI | 3 × [( |
[ |
PRI | ( |
[ |
NRI | ( |
[ |
GNDVI | ( |
[ |
PSRI | ( |
[ |
SIPI | ( |
[ |
NBNDVI | ( |
[ |
|
Maximum first-order differential value of the blue edge (490–530 nm) | [ |
|
Maximum first-order differential value of the yellow edge (550–582 nm) | |
|
Maximum first-order differential value of the red edge (670–737 nm) | |
SDb | The sum of first-order differential value of the blue edge | |
SD |
The sum of first-order differential value of the yellow edge | |
SDr | The sum of first-order differential value of the red edge | |
SDg | The sum of first-order differential value of the green peak (510–560 nm) | [ |
SDg/SDb | The ratio of SDg to SDb | |
SD |
The ratio of SD |
|
(SDr − SD |
Normalized ratio of SDr and SD |
|
(SDg − SDb)/(SDg + SDb) | Normalized ratio of SDg and SDb |
The unitary linear regression is a relationship between an independent variable and a dependent variable. It is the simplest regression method and also the basis for learning other regression methods. The model of the linear regression is [
Multiple stepwise regression algorithm is often used for regression analysis of two or more independent variables and dependent variables. The main principle is to determine its importance by the
It can be seen from Figure
Spectral reflectance curves of wheat ear with different scab severity.
It can be seen that the visible region is positively correlated with the severity of scab, and the near-infrared region is negatively correlated with the severity of scab (Figure
Curve of correlation coefficient between spectral reflectance and severity of disease.
According to the first-order differential spectral curve of four disease severities (Figure
The first-order spectral differential curves of different disease severities (350∼1330 nm).
Correlation analysis was used to assess whether significant relationships existed between the first-order differential and the wheat scab disease levels (Figure
Curves of correlation coefficient between first-order differential spectrum and disease severity.
According to the above analysis, the correlation between the original spectral data and the disease is not significant, while the first-order spectral differential has a good correlation with wheat scab. There are four significant correlations between first-order differential and disease severity during the 450–488, 500–540, 552–667, and 687–756 nm. The four wavelength ranges are located in the chlorophyll absorption zone, the blue edge, the yellow edge, and the red edge, respectively. When vegetation is infected by disease, the spectral reflectance of the chlorophyll absorption zone changes with the chlorophyll content. Relevant studies have shown [
Correlation of the first-order differential sum between four wavelength regions.
450–488 nm | 500–540 nm | 552–667 nm | 687–756 nm | |
---|---|---|---|---|
450–488 nm | 1 | |||
500–540 nm | −0.20839 | 1 | ||
552–667 nm | 0.696811 | −0.81765 | 1 | |
687–756 nm | −0.43428 | 0.746772 | −0.73471 | 1 |
The purpose of this experiment is to analyze the sensitivity of existing vegetation indices and spectral differential characteristics to wheat scab under linear regression conditions and to verify the applicability of the proposed scab index. Table
Unitary linear regression results of vegetation index and spectral differential characteristics.
Vegetation indices/spectral differential characteristics | Regression equations | Fitting |
Testing |
RMSE |
---|---|---|---|---|
NDVI |
|
0.66 | 0.62 | 15.90 |
RVSI |
|
0.46 | 0.41 | 18.97 |
TVI |
|
0.51 | 0.46 | 18.04 |
MCARI |
|
0.56 | 0.52 | 17.03 |
TCARI |
|
0.32 | 0.26 | 21.10 |
PRI |
|
0.23 | 0.17 | 22.39 |
NRI |
|
0.60 | 0.56 | 16.24 |
GNDVI |
|
0.29 | 0.22 | 21.74 |
PSRI |
|
0.63 | 0.60 | 15.64 |
SIPI |
|
0.63 | 0.59 | 15.72 |
NBNDVI |
|
0.67 | 0.63 | 14.85 |
SDb |
|
0.37 | 0.32 | 20.42 |
|
|
0.46 | 0.41 | 18.89 |
SD |
|
0.64 | 0.60 | 15.45 |
|
|
0.29 | 0.10 | 23.56 |
SDr |
|
0.42 | 0.37 | 19.54 |
|
|
0.48 | 0.44 | 18.45 |
SDg |
|
0.30 | 0.23 | 21.63 |
SDg/SDb |
|
0.26 | 0.16 | 22.82 |
SD |
|
0.61 | 0.58 | 15.88 |
(SDr − SD |
|
0.63 | 0.60 | 15.53 |
(SDg − SDb)/(SDg + SDb) |
|
0.28 | 0.19 | 22.25 |
WSI |
|
0.73 | 0.70 | 13.41 |
Related studies have shown that the model established by multiple variables is better than that by single variable [
Multiple stepwise regression model implemented in SPSS.
Model |
|
|
Error estimation | Predictor variable |
---|---|---|---|---|
1 | 0.853 | 0.73 | 13.13 | Constant, WSI |
2 | 0.865 | 0.75 | 12.80 | Constant, WSI, SDg/SDb |
3 | 0.877 | 0.77 | 12.41 | Constant, WSI, SDg/SDb, NBNDVI |
4 | 0.887 | 0.79 | 12.12 | Constant, WSI, SDg/SDb, NBNDVI, SDg |
The multiple regression model was compared with three unitary regression models with high fitting
Regression model. Unitary linear regression model constructed by (a) SD
Leave-one-out cross validation for (a) SD
The timely monitoring of wheat scab disease is critical for agricultural management. By analyzing the correlation between first-derivate spectra and corresponding disease severity levels, two sensitive wavebands (450–488 nm and 500–540 nm) were selected. Subsequently, a new index (WSI) was developed for detecting and monitoring wheat scab at the ear scale. Compared with other common spectral indices, WSI has excellent performance with the fitting
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
The work presented here was supported by the Anhui Provincial Science and Technology Project (16030701091), Natural Science Research Project of Anhui Provincial Education Department (KJ2019A0030), and National Natural Science Foundation of China (31971789).