Quantifying stripe rust reactions in wheat using a handheld NDVI remote sensor

BGRI 2013 Technical Workshop Proceedings 1922 August New Delhi, India 1

Quantifying stripe rust reactions in wheat using a handheld NDVI remote sensor

A. Arora1, R.K. Sharma1, M.S. Saharan1, K. Venkatesh1, N. Dilbaghi2, I. Sharma1 and R. Tiwari1*

1Directorate of Wheat Research, Karnal, Haryana 132001, India; 2Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125004, India

*Corresponding author: [email protected]

Abstract

Wheat crop production and productivity is significantly affected by the rusts with losses that impinge on national food security. A study for quantitating stripe rust reactions was performed on 120 Indian wheat genotypes representing released varieties, elite genotypes, genetic stocks and local landraces obtained from the Germplasm Unit of the Directorate of Wheat Research, Karnal. Stripe rust ephiphytotics were created with the Yr27-virulent Pst race 78S84. Rust reactions were recorded four times at seven day intervals. Area Under the Disease Progress Curve (AUDPC) values ranged from 0 to 2077. Since stripe rust infection affects foliar pigments through discoloration of green foliage color. We attempted to improve the precision of disease scoring by collecting data with a remote sensing based handheld Normalized Difference Vegetation Index (NDVI) sensor. This instrument reads the color of several plants and delivers an average value. The NDVI values in the present experiment varied from 0.46 to 0.69 across selected genotypes. A significant regression coefficient (r2 = 0.63) was observed between AUDPC and NDVI data. The results indicate that temporal ground-based NDVI data could be effective in quantitative rust reaction studies.

Keywords: Triticum aestivum, bread wheat, AUDPC, quantitative resistance, yellow rust

Introduction

Wheat is one of the worlds most important cereal crops with around 660 million tonnes of production during 2011-12 (http://www.fao.org/docrep/017/al998e/al998e.pdf). India had a record wheat production of 94.88 tonnes during the same year, the second largest production after China. However, production and productivity is affected by stripe rust. The pathogen Puccinia striiformis f. sp. tritici attacks leaves by forming yellow stripes, thereby reducing photosynthetic activity that may ultimately lead to grain shrivelling. Depending upon the disease severity and varietal differences, Chen (2005) reported 10 to 70% losses in wheat production due to stripe rust. Early and precise detection of the disease is needed to circumvent such losses.

Recent developments in optical sensor technology (West et al. 2003) have potential for direct quantitative detection of foliar diseases under field conditions. The main optical spectral domains (Fig. 1) in which green healthy leaves can show differences are: (a) Visible bands (400-700 nm): the lower reflectance region where light absorption is mainly dominated by plant pigments; blue (450 nm) and red (670 nm) form the two main bands, due to absorption by leaf pigments, chlorophyll a and b. These strong absorption bands induce a reflectance peak in the yellow-green (550 nm) band. (b) Near infrared region (700-1300 nm) in which the optical properties are explained by the leaf structure. High leaf reflectance and transmittance with low absorption is observed as leaf pigment and cellulose are transparent to near-infrared wavelengths. Photosynthetically active plant components, primarily leaves, produce a stepped reflectance


pattern with low reflectance in the visible region and high reflectance in the near infrared region (Rahman and Ahmed 2008).

Fig. 1 Spectral reflectance characteristics of green vegetation (after Hoffer and Johannsen 1969)

Spectral variations are due to differences in canopy architecture caused by disease, leading to chlorosis, presence of colored pustules, and other symptoms (Bravo et al. 2003). This has led to international interest in finding simple cost-effective optical means for remote sensing of disease at the earliest possible stage.

Various publications have described relationships between plant health and vegetation index. Normalized difference vegetation index (NDVI) was successful in predicting photosynthetic activity. NDVI is sensitive to the presence of vegetation, which absorbs radiant energy in the red band through chlorophyll, but reflects energy in the near infrared band (Verhulst and Govaerts 2010). Many authors have reported NDVI data obtained from hyperspectral imaging of plants and its correlation with various factors, including plant diseases (Bravo et al. 2003; Apan et al. 2004; Du et al. 2004; Moshou et al. 2004; Jacobi and Kuhbauch. 2005; Franke and Menz. 2007; Huang et al. 2007, 2012; Devadas et al. 2009; Kumar et al. 2010; Sankaran et al. 2010; Zhang et al. 2011 ), biomass (Vander Meer et al. 2000; Janin et al. 2009), plant stress (Carter and Knapp 2001), and yield prediction (Balaghi et al. 2008). According to some reports crop heterogeneity and vigor studies could be useful in reducing fungicide application by aiding decisions on the


position, timing, and concentration of spray application (West et al. 2003; Laudien et al. 2004; Larsolle and Hamid Muhammed 2005; Shaw and Kelley 2005).

Stripe rust is one of the most damaging diseases in wheat worldwide. Various studies have reported relations between spectral reflectance and rust infection (Moshou et al. 2004; Huang et al. 2007, 2012; Devadas et al. 2009; Zhang et al. 2011). An early disease detection system can aid in decreasing losses caused by plant diseases and can further prevent the spread of diseases (Sankaran et al. 2010) by timely control measures.

Most of the reports to now were based on spacial passive reflectance using satellites. However, many factors like atmospheric conditions, satellite geometry and calibration, scale of observation, soil background and crop canopy (Holben 1986; Justice et al. 1991; Soufflet et al. 1991) influence NDVI measurements. A solution to these problems may be handheld optical sensors such as GreenSeeker. As the sensor is an active device receiving the same light that it emits from its own source, measurements can be taken at any time during the day without atmospheric interference (Verhulst and Govaerts 2010).

Quantitative studies relating NDVI and AUDPC will not only help in detecting and controlling disease at an early stage, but can also be combined with molecular studies, permitting more precision as errors in recording disease incidence by visual estimation can be reduced.

Thus, the aim of the present study was to investigate the relationship between NDVI data obtained using the hand held active optical sensor GreenSeeker and stripe rust AUDPC scores in order to increase the precision of recording quantitative disease reactions.Material and methods

Plant material and field experiment

The present work was conducted at the Directorate of Wheat Research, Karnal. A set of 120 diverse wheat genotypes representing released varieties, elite genotypes, genetic stocks and local landraces were selected on the basis of SSR marker genotypes and pedigree. Seed was obtained from the germplasm unit of the Directorate of Wheat Research. Planting was done in an -lattice design replicated thrice with epiphytotic conditions supported by susceptible checks on both sides of the plots. The genotypes were segregated into groups based on physiological traits.

Inoculation and assessment of disease incidence

Cultivar Agra Local was used as a highly suseptible stripe rust spreader and was inoculated with the Yr27-virulent Puccinia striiformis race 78S84. The initial inoculum came from the Plant Pathology Laboratory within the Crop Protection Section of the Directorate of Wheat Research at Karnal.


Visual readings for stripe rust were taken at four weekly intervals after appearance of disease. Disease levels were recorded in the range of 0 to 100, where zero represented no stripe rust and 100 was the highest incidence on a whole plot basis. The Area Under the Disease Progress Curve (AUDPC) was calculated for each cultivar following Viljanen-Rohinson and Cromey (1998):

n AUDPC = 1/2 (Yi + Yi - 1) (Xi - Xi - 1) i = 1 where Yi = rust severity at the ith observation, Xi = time (d) at the ith observation, n = total number of observations, and Y0 = X0 = 0.

Equipment

NDVI data were recorded using the handheld active optical sensor known as GreenSeeker (Trimble Industries, Inc., USA). The NDVI value is computed from reflectance measurements in the red (around 660 nm) and near-infrared (around 780 nm) portions of the spectrum (Singh et al. 2011) using a patented technique to measure the fraction of emitted light in the target area that is returned to the sensors as crop reflectance:

NDVI = (RNIR

- RRed

) / (RNIR

+ RRed

)

where RNIR

is the reflectance of NIR radiation and RRed

is the reflectance of visible red radiation. The sensor displays a value in the range of 0.00 to 0.99.

The instrument scans the whole plot and delivers an average value as representative of the plot. The readings were taken twice weekly, after the first observation of stripe rust. The NDVI recorded is a direct indicator of the health of the crop; areas of greater photosynthetically active biomass (PAB), and higher yield vis-a-vis NDVI values.

To further enhance the precision of the present methodology, the effects of other factors like AUDPC range and plant height were also studied.

Results and discussion

Disease incidence was recorded visually and increased gradually over time from zero to 100 depending upon differences in stripe rust response. AUDPC computed for each genotype varied from 0 to 2077. The distribution of AUDPC scores is shown in Fig. 2. Similar numbers of genotypes occurred in all AUDPC categories, except the 1 100 group which included more entries (Table 1).


Fig. 2 Distribution of genotypes in AUDPC groups

Table 1 NDVI values after compensation for compounding effect of leaf blight AUDPC range No. of genotypes Uncorrected mean NDVI 0 14 0.69 1 - 100 55 0.66 101 - 200 12 0.64 201 - 500 13 0.65 501 - 1000 13 0.61 >1000 13 0.58

NDVI values were recorded when lines showed maximum stripe rust levels. Uncorrected NDVI values of 0.46 0.69 in Table 1 included a degree of interference from leaf blight. The mean values fell from 0.69 to 0.58 with increasing disease levels.

Change in reflectance measurements are caused by various physiological effects of the breakdown of chlorophyll pigments (Penueles et al. 1994) and with alterations in the concentrations of other pigments such as carotenoids and anthocynins (Young and Britton 1990; Gitelson et al. 2001).

Quantitative analysis

Linear regression analysis was carried out to examine the relationship of NDVI and rust incidence (AUDPC) in different wheat varieties. A significant r2 value of 0.63 between AUDPC and NDVI (Fig. 4a) indicated a substantial correlation. The regression equation (1) obtained for NDVI and AUDPC was:

NDVI = 0.663 6.165E-5(AUDPC)t ..(1)


Since the spectral quality of reflected light from leaves is manifested in leaf color, NDVI values were also affected by leaf blight that causes leaf browning. To eliminate the effect, values of NDVI were compared with those of the same genotypes treated with fungicides, and therefore having no rust or leaf blight symptoms. Error factors were calculated for the genotypes with different leaf blight scores and the raw NDVI scores were adjusted to more reliable values (Fig. 3). Different AUDPC values were adjusted by 0.08, 0.07, 0.07 0.08, 0.07 and 0.06 across the response classes. Application of the correction factors led to corresponding final NDVI values of 0.76, 0.73, 0.71, 0.73, 0.67 and 0.64, respectively (Fig. 3).

Fig. 3 Differences in NDVI values of before and after adjustment for the compounding effects of leaf blight


More encouraging results of r2 = 0.69 were obtained after removing the confounding effects of leaf blight. Regression equation (2) was obtained for the corrected NDVI values:

NDVI = 0.738 7.061E-5(AUDPC)t ....(2)

Figure 4a

Figure 4b

Fig. 4 Regression plots of AUDPC vs NDVI values before (4a) and after (4b) adjustment for the compounding effects of leaf blight

Previous authors (Bravo et al. 2003; Huang et al. 2007, 2012; Devadas et al. 2009; Moshou et al. 2011; Zhang et al. 2011) conducted studies using various spectroscopic and imaging techniques to view the accuracy in detection of stripe rust. In agreement with the earlier reports, the present


experimental results show a significant negative correlation between AUDPC and NDVI, confirming that remote sensing can be used for quantifying stripe rust levels in wheat.

Thus, not only encouraging remote sensing for detection and quantification of plant health, this study provides precise means through which the variations caused by atmospheric conditions can be minimized by utilizing the hand held instrument.

Factors affecting the correlation between AUDPC and NDVI

AUDPC score Correlation/regression analysis (Fig. 5) of AUDPC scores pertaining to individual genotypes in specific categories (range of AUDPC with that of NDVI value) showed an interesting pattern. As the category shifted from a lower range (resistant type) to a higher one (susceptible type) the correlation/regression coefficient became more significant (Table. 2). Therefore, as anticipated, the accuracy of NDVI sensor-based screening of disease response becomes increasingly reliable.

Table 2 Correlation of AUDPC and NDVI in different range categories of AUDPC

Figure 5a

AUDPC range NDVI range Coeff. of determination (r2)

Correlation coefficient

0 - 200 0.67-0.78 0.20 -0.45 >200 0.61-0.77 0.72 -0.85


Figure 5b

Fig. 5 Regression plots of AUDPC vs NDVI score based on genotypic distribution with different AUDPC ranges: (a) 0-200; (b) >200

Plant height

From genotypic scores based on height, a specific pattern was observed when the AUDPC scores in each category were regressed with the NDVI sensor data (Fig. 6a to 6f). Coefficients of determination in the range of 0.57 to 0.76 were obtained. Genotypes with height in the range of 115 - 124 cm showed an r2 value of 1.00. However, in the heights ranges of 75 84 and 85 94 cm, similar values (0.62) were observed. Significant correlations of 0.76 and 0.73 were obtained for genotypes in height ranges 65 74 and 95 104 cm, respectively. A value of 0.57 was observed when the range was 105 114 cm. Thus, by grouping genotypes on the basis of height, the experimental precision for scoring stripe rust reactions can be enhanced.

Figure 6a


Figure 6b

Figure 6c

Figure 6d


Figure 6e

Figure 6f

Fig. 6 Effect of plant height on regression plots of NDVI values as functions of AUDPC: (a) 65 74 cm (b) 75 84 cm (c) 85 94 cm (d) 95 104 cm (e) 105 114cm (f) 115 124 cm

Plant waxiness and growth habit

Waxiness and early growth habit had no effect on the correlation coefficients between AUDPC and NDVI. A significant and similar coefficient of around 0.72 was obtained for genotypes falling in different categories.

Conclusion

Sometimes it becomes difficult to discriminate small variations which are otherwise distinct responses because of genetic differences in rust reponse between genotypes. To capture these small variations, a phenotyping method with lower error was needed. In the present experiment


the possibility of utilizing a handheld NDVI sensor to aid rust workers was assessed as a means of improving the precision. In a set of 120 wheat genotypes, including released varieties, elite genotypes, genetic stocks and local landraces, responses to stripe rust were recorded both visually and by using a NDVI sensor. There was a significant correlation between AUDPC score and NDVI values indicating that this method could assist rust pathologists. Refining the data by reducing the confounding effects of physiological disturbances other than rust response (for example, other diseases, and chlorotic symptoms caused by abiotic stress) improved the precision of measurements. Plant characteristics such as growth habit and waxiness had no effect on the efficacy of using the NDVI sensor.

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Quantifying stripe rust reactions in wheat using a handheld NDVI remote sensor

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