Hyperspectral remote sensing of yellow mosaic severity and associated pigment losses in Vigna mungo using multinomial logistic regression models

Crop Protection 45 (2013) 132e140

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Hyperspectral remote sensing of yellow mosaic severity and associated pigment losses in Vigna mungo using multinomial logistic regression models Mathyam Prabhakar*, Yenumula G. Prasad, Suseelendra Desai, Merugu Thirupathi, Kondibaneni Gopika, G. Ramachandra Rao, Bandi Venkateswarlu Division of Crop Sciences, Central Research Institute for Dryland Agriculture, Santoshnagar, Hyderabad, Andhra Pradesh 500059, India

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 January 2012 Received in revised form 24 July 2012 Accepted 4 December 2012

Yellow mosaic disease (YMD) has been a serious threat to blackgram cultivation especially during postmonsoon season. Visual assessment of disease severity is qualitative and time consuming. Rapid and non-destructive estimation of YMD by hyperspectral remote sensing has not been attempted so far on any of its hosts. Field studies were conducted for two seasons with eight blackgram genotypes having differential response to YMD. Comparison of mean reflectance spectra of the healthy and YMD infested leaves showed changes in all the broad band regions. However, reflectance sensitivity analysis of the narrow-band hyperspectral data revealed a sharp increase in reflectance from the diseased leaves compare to healthy at 669 (red), 505 and 510 nm (blue). ANOVA showed a significant decrease in leaf chlorophyll (p < 0.0001) with increase in disease severity, while no such relationship was observed for relative water content. By plotting coefficients of determination (R2) between leaf chlorophyll and percent reflectance at one nm wavelength interval, two individual bands (R571; R705) and two band ratios (R571/R721; R705/R593) with highest R2 values were selected. These bands showed a significant linear relationship with SPAD chlorophyll readings (R2 range 0.781e0.814) and spectrometric estimates of total chlorophyll content (R2 range 0.477e0.565). Further, the relationship was stronger for band ratios compared to single bands. With optimal spectral reflectance ratios as inputs, disease prediction models were built using multinomial logistic regression (MLR) technique. Based on model fit statistics, reflectance ratios R571/R721 and R705/R593 were found better than the individual bands R571 and R705. Validation of MLR models using an independent test data set showed that the overall percentage of correct classification of the plant into one of the diseased categories was essentially same for both the ratios (68.75%). However, the MLR model using R705/R593 as dependent variable was of greater accuracy as it gave lower values of standard errors for slopes (bG range 9.79e36.73) and highly significant estimates of intercept and slope (p < 0.05). Thus the models developed in this study have potential use for rapid and non-destructive estimation of leaf chlorophyll and yellow mosaic disease severity in blackgram. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Blackgram disease Yellow mosaic virus Spectral reflectance Chlorophyll Spectroradiometry Logistic regression

1. Introduction Blackgram (Vigna mungo (L.) Hepper), also known as blackbean, urdbean and mushkalai is an important pulse crop traditionally cultivated in South Asia and adjoining regions. India is the major producer of blackgram in the world with an area of 3.24 million ha and a total production of 1.52 million tonnes (Anonymous, 2009).

* Corresponding author. Tel.: þ91 40 24530161; fax: þ91 40 24531802. E-mail addresses: [email protected], [email protected] (M. Prabhakar). 0261-2194/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cropro.2012.12.003

Being a legume and pulse, blackgram cultivation enriches soil nitrogen, and is an important source of dietary protein for millions of people in this region. In view of its short duration (65e85 days) this crop is taken up as a catch crop, intercrop and relay crop during rainy, spring and summer seasons. Yellow mosaic disease (YMD) caused by mungbean yellow mosaic virus (MYMV) is a major disease of blackgram, particularly during summer and spring seasons causing severe damage. Annual yield loss due to this disease in blackgram, mungbean and soybean together is estimated to be around US $ 300 million (Varma and Malathi, 2003). YMD was first reported on mungbean from India

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in the late 1940s (Nariani, 1960) and later on cowpea from Pakistan in the 1970s (Ahmad and Harwood, 1973). Presently the disease is widespread across India, Bangladesh, Pakistan, Sri Lanka and Thailand, becoming a major constraint to the production of most of the legume crops like mungbean (Vigna radiata), blackgram (V. mungo), pigeonpea (Cajanus cajan), soybean (Glycine max), mothbean (Vigna aconitifolia), common bean (Phaseolus vulgaris) and yard-long bean (Vigna sesquipedalis) (Qazi et al., 2007). Indian YMD is caused by a bipartite begomovirus which is not transmitted through seed and soil but was found to be transmitted persistently by whitefly, Bemisia tabaci (Ahmad and Harwood, 1973). Yield reduction up to 77 percent has been reported in blackgram due to this disease (Haq et al., 1992). A severe outbreak of YMD in mungbean during 1997 in northern Thailand caused major losses to its production resulting in shift of cropping practices (Qazi et al., 2007). An estimated twenty five thousand hectares of blackgram was affected by YMD in Andhra Pradesh, India during the year 2010 (Anonymous, 2010). Virus infection in plants could manifest into one or many symptoms such as changes in leaf pigmentation, leaf curling, wilting, stunting, chlorosis, necrosis or abscission of plant parts. Plants infected with YMD initially show light interspersed yellow patches on the leaves, which later turn to yellow and the subsequent new leaves show necrosis (Fig. 1) with shortening of internodes, and severe stunting (Table 1). YMD affected plants produced few flowers and deformed pods with immature and shrivelled seeds (Akhtar et al., 2011). Though many of these symptoms can be visually observed, it is still not easy to diagnose early with precision and quantify the stress. The YMD disease severity is assessed based on visual scoring of the infected plants (Bashir et al., 2006; Habib et al., 2007), which is qualitative and often subjective. Remote sensing helps in quantification of crop stress with more precision than visual methods, and can be used for repeated measurements non-destructively and noninvasively (West et al., 2003; Nilsson, 1995). A few studies have demonstrated the feasibility of using remote sensing data for detection of virus diseases like grape leaf roll virus (Naidu et al., 2009), tobacco virus (Krezhova et al., 2009), tomato mosaic virus (Krezhova et al., 2010), wheat yellow dwarf and streak mosaic (Mirik et al., 2011; Riedell et al., 2003), potato yellow vein virus (Chavez et al., 2011), and sugarcane yellow leaf virus (Grisham et al., 2010). However, no such information is available for YMD on any of its hosts. Keeping in view the wide host range and rapid spread of YMD in several countries we conducted this study with the following objectives: (i) Characterise reflectance spectra of blackgram under different levels of yellow mosaic disease severity, (ii) Establish functional relationship between disease severity with chlorophyll (Chl) pigments, relative water content (RWC), and their association with reflectance spectra (iii) Identify optimal narrow spectral bands and band ratios for assessing disease severity using ground based hyperspectral radiometry and, (iv) Develop suitable models for assessing YMD severity using spectral data.

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Table 1 Symptoms of damage by yellow mosaic disease on blackgram. Healthy Mosaic Pucker Yellow Stunt

No symptoms Irregular green and yellow patches scattered on the leaf lamina Raised green areas on leaves along with large mosaic patches Complete yellowing of the leaves that gradually change to whitish shade Necrosis, reduction in leaf size and stunted internodes

2. Materials and methods Data for this study was recorded from a varietal screening experiment of eight blackgram cultivars (KDRS-251, PSR-2541B, KARS-269, CN-9013, L2, SK-023, T-8, LBG-20) assessed for different traits during two seasons, i.e., spring and summer, 2011 at the Central Research Institute for Dryland Agriculture, Hyderabad, India (17 200 54.42700 N, 78 290 59.19200 E). Spring crop was sown on 20 February, 2011 and the summer crop on 25 March, 2011. The crop was raised in plots measuring 3 m2 and the experiment was laid in a randomised block design with three replications. A spacing of 30  10 cm was adopted to maintain optimum population of 33 plants/m2. Soil in the experimental site was shallow (30e50 cm), typic haplustalf (red sandy loam) with organic carbon content of 0.4 percent. The crop was raised by following all recommended agronomic practices. No pesticide was applied in both the seasons allowing natural infestation and spread of YMD. Additionally, three separate blocks of 3 m2 with YMD tolerant cv. LBG-20 were maintained pest and disease free by seed treatment with carbendazim @ 2 g/kg seed and alternate prophylactic sprays of insecticides, oxydemeton methyl 25 EC @ 0.03 percent and acetamiprid 20 SP @ 0.005 percent, at fortnightly interval starting 30 days after sowing. Sufficient numbers of blackgram plants with varying levels of YMD symptoms were available in these plots along with healthy plants, permitting a wide range of variation required for performing statistical analysis.

2.1. Data recording 2.1.1. Leaf spectral reflectance Leaf spectral data was recorded using a plant probe attached to a portable hyperspectral radiometer (ASD FieldSpec 3 Hi-Res, Boulder, USA) with a spectral resolution (full-width-halfmaximum, the narrowest spectral feature that can be resolved by a spectrometer) of 3 nm at 700 nm, 8.5 nm at 1400 nm, and 6.5 nm at 2100 nm. The sampling interval varied at different regions of electromagnetic spectrum (1.4 nm at 350e1000 nm range, and 2 nm at 1000e2500 nm range). The instrument was warmed-up for 15 min prior to measurements in order to avoid spectral steps at detector overlap wavelength region, which occur due to different warm-up rates for the three spectroradiometer arrays. It is

Fig. 1. Symptoms of yellow mosaic disease on blackgram leaves: healthy (A), mosaic (B), puckering (C), yellowing (D) and stunting (E).

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necessary to optimise the spectrometer at regular intervals so that changing levels of irradiance do not cause the detectors to saturate, and sensitivity of the instrument adjusts according to the specific illumination conditions at the time of measurement. Therefore, optimisation of radiometer was done using spectralon white reference panel (ASD, Boulder, USA). Plant probe with an in-built 100 W reflectorised halogen lamp provided the versatility of an internal light source for the spectroradiometer and allowed spectral data collection regardless of weather and time of day. White reference scans were collected using the spectralon white reference panel and every surface measured by the fibre optic was saved as a ratio between digital number (DN) of the surface relative to the ratio between DN of the spectralon white reference. Spectral data was recorded from adaxial leaf surface of upper most fully expanded intact leaf. The 25 field of view (FOV) of spectroradiometer and leaf clip assembly in the plant probe allowed reflectance measurement from a leaf spot measuring 10 mm diameter. The first appearance of YMD in the experimental block was observed 3e4 weeks after sowing. Spectral data was recorded about two weeks after the first appearance of disease, i.e., on 11 April and 4 May, 2011 for the spring and summer crops, respectively. Sampling of the plants for data recording was done randomly across the plots for each genotype. Depending on the appearance of symptoms on leaves, disease was categorised into five severity grades viz., healthy, mosaic, puckering, complete yellowing and stunting so that the scoring could be correlated with chlorophyll content (Table 1, Fig. 1). Each time about 10e 25 plants were selected from each disease grade, and the spectral data was recorded from a total of 109 and 80 plants during spring and summer seasons, respectively. The measurements from each of the leaflets on a trifoliate were averaged for each plant sample. Resultant data were interpolated using ASD ViewSpecPro software (Version 6.0.15) to get values at one nm increments (ASD, 1999). 2.1.2. Chlorophyll content (Chl) Immediately following measurement of reflectance, leaf chlorophyll was estimated from the same leaf sample. A portable chlorophyll content meter (SPAD-502, Minolta Corp., Japan) was used in the spring season due to non availability of spectrophotometer during that period, while spectrophotometric estimation of chlorophyll was done during summer season. SPAD chlorophyll index data was measured (n ¼ 107) by averaging five readings per leaf sample. For spectrophotometric estimation of chlorophyll, leaf discs measuring 10 mm diameter were punched from the same portion of leaf where reflectance was recorded previously by plant probe. These samples (n ¼ 75) were immediately transferred to vials with 15 ml dimethyl sulphoxide (DMSO) and incubated at room temperature in the dark for 24 h. Absorbance of the clear extracts was measured using Genesys UV/VIS spectrophotometer (Thermospectronic, Rochester, USA) at 662, 646 and 470 nm for Chl a, Chl b and total chlorophyll, respectively (Porra et al., 1989). Concentration of leaf chlorophyll was expressed as mg/cm2 of leaf. 2.1.3. Relative water content (RWC) After punching leaf disc sample for chlorophyll, the remaining part of the leaf was kept in a sealed polythene cover in an ice box and transferred to the laboratory for further analysis. After recording the fresh mass (FM), samples were cut into small pieces and soaked in water for 4 h to record turgid mass (TM). Thereafter, samples were dried at 80  C for about 48 h to determine dry mass (DM). RWC was calculated as described by Smart and Bingham (1976).

RWC ¼ ½ðFM  DMÞ=ðTM  DMÞ  100

RWC estimations were done for only samples from summer crop (n ¼ 75). 2.2. Data analysis The digital number (DN) values were converted to reflectance using the ASD ViewSpecPro software (ASD, 1999). The one wayANOVA was performed using ‘PROC GLM’ by taking YMD severity as independent (class) variable and leaf chlorophyll concentration (Chl a, Chl b, Chl a þ b, ratio of Chl a/b) and RWC as dependent (response) variables. The means were separated using Tukey’s HSD test (a ¼ 0.05%) (SAS Inst., 2009). We followed two approaches to discern the voluminous hyperspectral data (2151 data points for each spectra) into a very few meaningful bands. In the first approach, relative difference in the reflectance due to disease was measured by calculating reflectance sensitivity at each nm wavelength. As the objective of sensitivity analysis was to identify few bands based on the peak value of the reflectance sensitivity, but not to seek the variation in the peak value itself, we have considered the ‘average’ reflectance values for each disease grade instead of individual ‘replicates’. The reflectance difference (RD) and reflectance sensitivity (RS) were calculated as described by Riedell et al. (2003).

RD ¼ ðReflectance of diseased leavesÞ  ðReflectance of the healthy leavesÞ: RS ¼ ½RD=ðReflectance of the healthy leavesÞ  100: In the second approach, data from both the seasons was pooled and coefficients of determination (R2) were used to evaluate linear relationships between chlorophyll (SPAD readings & Chl a þ b) and percent reflectance at every one nm interval throughout the range of 350e2500 nm (Fig. 3). Thereafter, the reflectance values with greatest R2 (571 & 705 nm) were used as the numerators and values at all other wavelengths (Ri) as denominators to calculate reflectance ratios (R571/Ri & R705/Ri), and the R2 values of the reflectance ratios with leaf chlorophyll were further determined (Zhao et al., 2005a). The reflectance ratios which had the greatest R2 values (R571/R721 & R705/R593) were selected as optimum ratios. Further, functional relationship of single band reflectance (R571 & R705) and reflectance ratios (R571/R721 & R705/R593) with chlorophyll (SPAD readings & Chl a þ b) was established through SAS using ‘PROC REG’ (SAS Inst., 2009). In this way, from a total of 2151 bands (from 350 to 2500 nm) we arrived at only four individual bands (571, 593, 705 & 721 nm) and two band combinations (R571/R721 & R705/R593) which are most appropriate for remote sensing of blackgram mosaic disease severity. These identified bands and band ratios were further considered to build suitable models to classify the disease severity levels. The multinomial logistic (MLR) regression model, which is an extension of binary logistic regression model provides a solution to the principal question of identifying which among the spectral bands and band ratios is more important in differentiating the five nominal responses (grades) namely healthy, mosaic, puckered, yellow and stunted. In MLR analysis if the response variable or the dependent variable has several categories, one of them is designated as the reference category and the probability of membership in other categories is compared to the probability of membership in the reference category. In our case, the dependent variable ‘grade’ had five categories. ‘Healthy’ is taken as the reference category. Thus, MLR gave four equations, one for each non-referential category relative to the category ‘healthy’, which is the reference category, to describe the relationship between the dependent

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Fig. 2. Mean reflectance spectra of blackgram leaves under different levels of yellow mosaic disease severity (top), reflectance difference of disease levels from healthy (middle) and reflectance sensitivity to disease infestation (bottom).

variable ‘grade’ and the independent variable ‘reflectance’. The generic form of the equation is,

ln

PðGrade ¼ GÞ ¼ aG þ bG  Ri ¼ ZiG PðGrade ¼ HÞ

where, disease grade (G) ¼ H, M, P, Y and S, representing healthy, mosaic, pucker, yellow and stunt, respectively; aG (intercept) and bG (slope) are the regression coefficients. Hence, considering ‘healthy’ as the reference category we get four equations, one for each non-

referential category of disease grade, G (M, P, Y & S) that gives the log of odds ratio of the ith observation for that value of G. Probability that the ith observation belonged to category G ¼ M, P, Y or S was obtained from,

PrðGrade ¼ GÞ ¼ ¼

1þ

expðZiG Þ P G expðZiG Þ

expðZG Þ 1 þ expðZiM Þ þ expðZiP Þ þ expðZiS Þ þ expðZiY Þ (1)

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independent data set to classify samples into different grades using the predicted probabilities given by the model. Model fit statistics viz., akaike information criterion (AIC), schwarz criterion (SC), coefficient of determination (R2) and Max-Rescaled R2 were calculated to explain variation in the response variables. 3. Results 3.1. Effect of YMD on reflectance spectra Analysis of narrowband hyperspectral data showed the highest reflectance difference at 689 and 685 nm, followed by 647 and 644 nm (all in red region) for spring and summer seasons, respectively (Fig. 2). Reflectance sensitivity analysis revealed a peak value at 669 nm in both the seasons followed by a second peak at 510 and 505 nm during spring and summer seasons, respectively (Fig. 2). These results indicated that the visible region between 500 and 705 nm was more affected due to YMD in blackgram compared to other regions viz., NIR (760e900 nm) and mid SWIR (1550e 1750 nm). 3.2. Effect of YMD on leaf chlorophyll and relative water content There was a significant reduction in Chl a, b and Chl a þ b due to YMD in all the disease severity classes compared to healthy leaves. Similar trend was observed in SPAD readings between different disease severity grades (Table 2). However, there was no significant difference in Chl a/b ratio among healthy, mosaic and puckering, while it decreased significantly in yellowing and stunting. Though stunting is the most severe form of YMD disease, Chl content (Chl a þ b & SPAD readings) in stunted leaves was significantly higher than leaves showing yellowing symptoms. Relative water content was similar in all the disease severity classes, except in stunting (Table 2). 3.3. Functional relation between spectral bands and chlorophyll The two single reflectance bands identified (571&705 nm) using linear relationships between YMD severity and percent reflectance and the subsequent sensitive band ratios identified (R571/R721 & R705/R593) (Fig. 3) were used to evaluate their functional relationship with leaf chlorophyll (SPAD readings in spring, and Chl a þ b in summer). In both the seasons, this relationship was highly significant (R2 range 0.477e0.814, p < 0.001) (Fig. 4). Improvement in R2 was observed when the band ratios were used in place of single bands. Also the relationship between spectral bands and leaf chlorophyll was stronger for SPAD readings (R2 range 0.781e0.814) Fig. 3. Coefficient of determination vs. wavelength for the relationship between chlorophyll and reflectance at all wavelengths (top), reflectance ratios of R571/Ri (middle), and R705/Ri (bottom).

And the probability (Pr) that the ith observation belonged to the reference category was obtained from the expression,

PrðGrade ¼ HÞ ¼ ¼

1þ

P

1

G expðZiG Þ

1 1 þ expðZiM Þ þ expðZiP Þ þ expðZiS Þ þ expðZiY Þ (2)

The MLR models were built from the spring season data (n ¼ 109) using ‘PROC LOGISTIC’ (SAS Inst., 2009). Finally the models were validated using the summer season (n ¼ 80) as an

Table 2 Effect of YMD on chlorophyll (Chl) and relative water content (RWC) in blackgram leaves. Disease severity Chl a Chl b Chl a þ b Chl grade (mg/cm2) (mg/cm2) (mg/cm2) a/b Healthy Mosaic Pucker Yellow Stunt CV F ratio df Pr > F

SPAD reading

RWC (%)

32.46a 8.36a 40.82a 3.90a 41.49a 71.71a 30.63b 7.23b 37.33b 4.23a 23.08b 65.98ab 19.28c 4.96c 24.24c 3.82a 13.71c 69.34a 0.95e 1.19d 2.15e 0.85c 0.0e 71.71a 6.42d 2.03cd 8.45d 3.03b 7.61d 56.55b 32.46 28.99 31.33 18.75 30.59 14.00 106.12 97.08 106.68 77.72 202.36 6.63 4,70 4,70 4,70 4,70 4,102 4,70 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.002

Means within a column followed by the same letter are not significantly different using Tukey’s HSD test (a ¼ 0.05); CV: Coefficient of variation; df: degrees of freedom.

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Fig. 4. Linear regression of leaf chlorophyll (SPAD readings & Chl a þ b) with single band reflectance and reflectance ratios.

compared to spectrophotometric estimation of chlorophyll (R2 range 0.477e0.566). 3.4. Selection of optimum model for disease classification The few hyperspectral bands that are specific to blackgram YMD identified based on R2 approach were considered for model building because in this method in addition to individual bands we also get the best band ratios. Both the identified bands (R571, R705) and band ratios (R571/R721, R705/R593) were separately considered to build the multinomial logistic regression models. Analysis of effects (Table 3) showed that all four models were statistically significant (p < 0.0001). Furthermore, the model fit

statistics (AIC, SC, R2 & Max-Rescaled R2) suggested that the reflectance ratios R571/R721 and R705/R593 better explained the variation in the response variable ‘Grade’ than the individual reflectance bands R571 and R705. The Max-Rescaled R2 for R571/ R721 and R705/R593 were 0.91 and 0.90, respectively. The standard errors of the parameter estimates of intercept (aG) and slope (bG) for R705/R593 ratio were comparatively lower (Table 4), hence was considered more reliable than R571/R721. All the estimates of intercept (aG) and slope (bG) for the MLR model using R705/R593 as dependent variable were also statistically significant (p < 0.05). Thus, our study reveals that the reflectance ratio R705/ R593 could be taken as the best choice for grading blackgram YMD severity.

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669 nm (red) followed by peaks at 505 and 510 nm (blue). Similar analysis by Riedell et al. (2003) for wheat streak mosaic virus revealed peaks in the regions of red (630e640 nm) and blue (450e 460 nm). Shift in the band positions for the peaks within the same broad band region could be due to the differences in host-pathogen systems (blackgram-MYMV interactions) used in this study. Earlier studies have shown that leaf reflectance in the wavelengths between 500 and 750 nm were closely associated with leaf chlorophyll content (Zhao et al., 2005a,b; Read et al., 2002; Daughtry et al., 2000). This could be further corroborated with our findings on reduction of leaf pigments, particularly leaf chlorophyll, with increased mosaic disease severity (Table 2, Fig. 4). Similar reduction of chlorophyll due to viral diseases have been reported in mustard (Guo et al., 2005), banana (Hooks et al., 2008), sugarcane (Grisham et al., 2010), tobacco and tomato (Krezhova et al., 2009, 2010). However, non-significant reduction in RWC between different grades except stunting (Table 2) could be substantiated by the low reflectance difference and reflectance sensitivity values in the major moisture absorption bands centred around 970, 1200, 1400, 1450 and 1940 nm (Fig. 2). Since the differences in leaf chlorophyll content between the disease classes is highly significant (p < 0.0001), we used this data to establish a linear relationship with percent reflectance. Of all the 2151 bands between 350 and 2500 nm tested, reflectance of individual bands at 571 (R571) and 705 nm (R705), and band ratios of R571/R721 and R705/R593 showed high coefficient of determination values (R2) with leaf chlorophyll and hence were considered as optimal bands for assessing YMD severity (Fig. 3). Similar approach of using band ratios to determine nitrogen stress has been reported in cotton (Zhao et al., 2005a; Read et al., 2002) and sorghum (Zhao et al., 2005b). Further, the significant linear relationship of these bands with chlorophyll content in these studies is similar to the findings from our study (Fig. 4). Earlier studies showed reflectance ratios of R705/R930 (in cotton by Read et al., 2002), R700/R750 (in tobacco by Lichtenthaler et al., 1996) and R715/R445 (in sorghum by Zhao et al., 2005b) fit a linear relationship with chlorophyll, and hence were reported as optimum ratios. In case of wheat, the ratio between sum of 1st derivatives within red region (SDr) and green region (SDg) was highly correlated with Chl a (Li et al., 2007). Carter and Spiering (2002) working with several tree species found that chlorophyll concentration was associated with reflectance at 549 and 715 nm, but followed a power function than a linear function. A strong association of leaf chlorophyll with the band R705 found in our study is close to one of the optimum bands reported by earlier investigators (Zhao et al., 2005a,b; Lichtenthaler et al., 1996; Carter and Spiering, 2002), but differed in other band positions, presumably because of the difference in host-pathogen interactions.

Table 3 Multinomial logistic regression model fit statistics. Statistics

Spectral bands and band ratios R571

R705

AIC 153.71 159.25 SC 175.24 180.78 2 Log L 137.71 143.25 R2 0.86 0.85 Max-rescaled R2 0.89 0.88 Analysis of Effects (Testing Global Null Hypothesis: df 4 4 Wald c2 25.69 30.09 Pr > c2 <0.0001 <0.0001

R571/R721

R705/R593

136.41 157.93 120.41 0.88 0.91 Slope ¼ 0) 4 22.78 <0.0001

149.69 171.22 133.69 0.86 0.90 4 28.08 <0.0001

AIC: akaike information criterion; SC: schwarz criterion; R2: coefficient of determination; df: degrees of freedom.

By substituting the parameter estimates (Table 4) in Equations (1) and (2), the probabilities of belongingness of given disease severity category to a specific value of the reflectance were obtained. These predicted probabilities were plotted in Fig. 5 for the training dataset (spring season). Validation of these MLR models (Table 5) using independent test data set (summer season) showed that when the disease incidence was low the MLR model with ratio R571/R721 was able to better predict the grade (healthy: 100%, mosaic: 47.62%). And when the disease incidence was high, R705/ R593 performed better (pucker: 75%, yellow: 100%, and stunt: 40%). However, the overall percentage of correctly classifying the plant into one of the categories was essentially same for both the ratios (68.75%). 4. Discussion Damage due to pests and diseases interfere with morphological and physiological traits of plants affecting absorption of light energy and thus leading to alteration in the reflectance spectrum (Chaerle and Van Der Straeten, 2000; Hatfield and Pinter, 1993). Comparison of reflectance between healthy and YMD affected blackgram leaves in our study showed differences in the broad band regions of the spectra (Fig. 2). Similar observations were made in tobacco while comparing healthy and mosaic virus infested leaves for all the bands except NIR (Krezhova et al., 2009, 2010). In wheat under yellow rust disease stress, maximum correlation of chlorophyll with leaf spectral reflectance was observed at 708 nm, followed by 766 nm (Li et al., 2007). Grapevine leaf roll virus infested leaves showed maximum difference in reflectance at 550 and 680 nm (Naidu et al., 2009). However, reflectance sensitivity analysis using the narrowband hyperspectral data in our study suggested a rapid increase in reflectance at visible region with increase in YMD severity (Fig. 2), specifically a sharp peak at

Table 4 Analysis of maximum likelihood estimates of the MLR models using spectral band ratios as independent variables.

aG R571/R721 Mosaic Pucker Yellow Stunt R705/R593 Mosaic Pucker Yellow Stunt

bG Wald c

Pr > c

39.031 39.140 39.114 57.531

2.595 3.576 3.257 7.866

0.107 0.059 0.071 0.005

173.700 192.600 187.500 294.600

109.500 109.600 109.600 119.900

2.516 3.087 2.926 6.034

0.113 0.079 0.087 0.014

18.207 18.743 18.602 43.385

6.363 11.007 8.862 13.862

0.012 0.001 0.003 0.000

25.185 36.594 31.492 125.200

9.791 10.265 10.105 36.729

6.617 12.709 9.713 11.623

0.010 0.000 0.002 0.001

Estimate

SE

62.878 74.017 70.585 161.400 45.927 62.183 55.376 161.500

2

2

Estimate

‘G’ indexes the disease severity levels (mosaic, pucker, stunt and yellow); aG: Intercept; bG: Slope; SE: Standard error.

SE

Wald c2

Pr > c2

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139

Fig. 5. Predicted probabilities of yellow mosaic disease severity using single reflectance bands (top) and band ratios (bottom) as input variables in the multinomial logistic regression models.

Several models have been suggested using partial least squares (PLS) regression, principal component regression (PCR), decision tree or tree-based modelling (TBM), artificial neural networks (ANN), and support vector machines (SVM) to classify plant sample into different disease classes using spectral reflectance as an input (Rumpf et al., 2010; Liu et al., 2010; Naidu et al., 2009; Delalieux et al., 2007). However, we chose multinomial logistic regression (MLR) technique due to the nature of response variable in our study and easy interpretation of results based on physiological changes (Delalieux et al., 2007). The response variable used in our study was multinominal (five disease grades) unlike binary variables

Table 5 Classification accuracy of the multinomial logistic models using identified band ratios (R705/R593 and R571/R721). Observed Predicted Healthy Mosaic Pucker Stunt Yellow Percent correct R705/R593 Healthy Mosaic Pucker Yellow Stunt Overall percentage R571/R721 Healthy Mosaic Pucker Yellow Stunt Overall percentage Sample size, n ¼ 80.

19 21 20 10 10

18 2 0 0 0 25.00

1 8 0 0 2 13.75

0 4 15 0 4 28.75

0 0 7 0 3 2 0 10 4 0 17.50 15.00

94.74 38.09 75.00 100.00 40.00 68.75

19 21 20 10 10

19 0 0 0 0 23.75

0 10 0 0 2 15.00

0 4 14 0 6 30.00

0 0 7 0 4 2 0 10 2 0 16.25 15.00

100.00 47.62 70.00 100.00 20.00 68.75

(infected, non-infected) by Delalieux et al. (2007). The MLR models built with training data set from one season were tested using test data of the subsequent season. The better predictive probabilities of the models with band ratios compared to single bands (Table 3, Fig. 5) is in conformity with the earlier reports (Naidu et al., 2009; Li et al., 2007). The model fit statistics (Table 3) and high classification accuracy of the model when tested with independent data set (Table 5) suggested the robust performance of the proposed model for use in assessing YMD severity in blackgram (Fig. 5). Also the reflectance data obtained by such ground based remote sensing studies could offer vital information to understand the interactions between pest damage and the host plants; provide fundamental ground-truth information required for interpretation of remote sensing data obtained from air and space borne platforms, thereby, guide scouting efforts and crop protection advisory for large areas in a more precise and effective manner (Prabhakar et al., 2012). However, due to late appearances of spectral difference in the visible domain, an in-depth study of the near-infrared domain is required for early detection of plant stress (Delalieux et al., 2007). 5. Conclusions This study characterised reflectance spectra of blackgram leaves under stress due to yellow mosaic disease. The new hyperspectral bands and band ratios identified in this study demonstrated their use in remote sensing of chlorophyll in blackgram. Further, by using the spectral data in the MLR models it was possible to assess the yellow mosaic disease severity with reasonable degree of accuracy. Therefore, by using the narrowband reflectance ratio of R705/R593 it should be possible to rapidly screen large number of cultivars of blackgram for their reaction to YMD. Also, results of this study would provide valuable information for the use of remote sensing

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