Color sensing and image processing-based ...

Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation Sourabh Shrivastava, Satish Kumar Singh & Dhara Singh Hooda

Multimedia Tools and Applications An International Journal ISSN 1380-7501 Volume 74 Number 24 Multimed Tools Appl (2015) 74:11467-11484 DOI 10.1007/s11042-014-2239-0

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Author's personal copy Multimed Tools Appl (2015) 74:11467–11484 DOI 10.1007/s11042-014-2239-0

Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation Sourabh Shrivastava & Satish Kumar Singh & Dhara Singh Hooda

Received: 26 October 2013 / Revised: 5 July 2014 / Accepted: 18 August 2014 / Published online: 19 September 2014 # Springer Science+Business Media New York 2014

Abstract Soybean is among one of the most important commercial crops, which is cultivated worldwide. The research work presented in this paper is focused on the problems associated with the cultivation and highlights the effect of various Soya plant foliar diseases on its yield. It has been presented a fully automatic disease detection and level estimation system which is based on color image sensing and processing. Various new parameters, namely DiseaseSeverity-Index (DSI), Infection-Per Region (IPR), and Disease-Level-Parameter (DLP) for measuring the disease severity level and level-classification have also been formulated and derived. The proposed method has been tested on a real database of Soya leaves collected between July 2012 and September 2012 and found to be at an excellent methodology for the purpose mentioned above. Experimentation has shown that the method is superior to the methods proposed by Cui et al. (Sens & Instrumen. Food Qual. 3(1),49–56, 2009) & (Biosyst Eng. 107(3), 186–193, 2010) in terms of adopted methodology and measuring parameters used. Keywords Soya-plant foliar disease . Rust . Bacterial blight . Brown spot . Sudden death syndrome . Frog’s eye . Downy mildew . Automatic disease identification . Disease level classification

1 Introduction One of the most important commercial crops, i.e. soybean is grown in various countries namely, USA, Brazil, Argentina, China and India, contributing 38, 29, 22, 6 and 5 % respectively in total world soya production. The data published through the report by the Food and Agriculture Organization of the United Nations 2012 (Food and Agriculture Report Electronic database, 2012) shows that the production of soybean is interestingly increasing on a yearly basis [8]. S. Shrivastava (*) : D. S. Hooda Jaypee University of Engineering and Technology, Guna 473 226 MP, India e-mail: [email protected] S. K. Singh Indian Institute of Information Technology, Allahabad 211 012 UP, India

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Multimed Tools Appl (2015) 74:11467–11484

The report, published by soybean processors association (SOPA) in 2010 is clearly showing that the production area for soybean in India has dramatically increased from 608,000 Ha in 1980 to 12.6 M-Ha in 2010 [21]. A massive increase in the area of cultivation is the main reason for increase in gross production. The year-on-year average yield is almost constant around 1,000 Kg/Ha, which is less than half of the yield in USA, Brazil, Argentina and China [7]. India receives the rank in the top ten Gross Domestic Product (GDP) countries and agricultural sector cannot be overlooked because more than 50 % of the workforce in India is employed exclusively in this sector from last 30–40 years, whereas the contribution of this sector in GDP is the decreasing from 23 % in 2001 to 17 % in 2011. Despite the huge area of cultivation and major workforce involvement, the yield is not very much distinctive for the developing countries like India. This is giving the scope for research in this area. The annual increase in the price of production due to increase in the region of cultivation will have a negative impact on a large number of farmers engaged in this soybean cultivation business. There are many causes for low production, namely plant diseases, bugs’/insects’ attacks, climate and environmental conditions and most importantly the lack of necessary knowledge and consciousness among the farmers. The knowledge among the farmers can be reinforced with the help of data engineering and associated tools & techniques in several sectors of agriculture like watershed management, natural disaster management, and bug & disease management to raise the productivity. The research study described in this paper focus the evolution of automatic Soya bug and disease administration using color sensor and image processing techniques. The commercial farmers in developing nations applied the proper knowledge base and latest technology to handle this difficult situation brought up because of high input cost, whereas it is not so in the developing countries like India. This research is targeted to meet the gap between commercial farmers in developed and developing countries and to provide with the latest technological aid. The development of soybean has been split into two stages, namely, vegetative and reproductive [4]. The ontogeny and development under both the aforementioned stages depend on diverse factors like, rain, humidity, temperature, and day-length. Vegetative stage is defined between the times of emergence and flowering, and the height of the plant is developed up-to 12–14 in.. After the vegetative stage the reproductive phase, which is the most significant one, starts from the launch of flower in the plant [4]. The early and late-reproductive stages are responsible for determining the number and the quality (shape and size) of the seeds respectively. The soya bean production is primarily involved in vegetative and early reproductive stages and the effects are very severe if infected with any disease during these phases. Yield loss in vegetative state is roughly 3 and 97 % in reproductive stage. In literature, it has been described that the diseases which are responsible for yield loss are mainly the plant foliar diseases, where the major soybean plant diseases are soybean-rust, bacterial-blight, suddendeath-syndrome, downy-mildew, frog eye, and brown-spot causing 10–90 %, 0–15 %, 0– 100 %, 9–18 %, 10–60 % and 8–10 % yield loss, respectively ([10, 11, 14, 15–18, 21–24, 27–29]). So the early detection of the diseases as shown in Table 1 and corresponding preventive measures can minimize the yield loss significantly. In the following paragraphs, we provide a brief overview of various research works which are directly or indirectly related to foliar disease detection either for soybean or for some other crop. Cui et al. [3] proposed a method for soybean rust detection using reflectance of the multispectral images and various image processing algorithms. The portable Spectroradiometer and a CCD camera were used for image data collection by Cui et al. [3]. The rust severity level was estimated by them. They reported that the lowest level of reflectance is shown in the severely infected soya plant leaves in the reflectance spectrum

Author's personal copy Multimed Tools Appl (2015) 74:11467–11484

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Table 1 aSix major soya plant foliar diseases: Soybean Rust [15], Bacterial Blight [16], Sudden Death Syndrome [18, 27], Downy Mildew [1, 20], Brown Spot [4, 12, 13] & Frog Eye [6, 14, 26]

[a http://www.extension.umn.edu/cropdiseases/soybean]

while analyzing the multispectral diseased and healthy leaves. Cui et al. [3] has also reported few parameters for quantifying the rusting effect in soya plant leaves. These proposed parameters are the lesion color index (LCI), the infected area’s ratio (RIA) and severity index for rust (RSI). Potency of the method was examined on the RGB color space. In addition, the method proposed by Cui et al. [3] is manual and requires human intervention. The method proposed by us is targeted to overcome the problems associated with the earlier reported methods ([2, 3], Dorence et al. [5, 6]) and are summarized as follows, 1. The earlier reported literature [2, 3] concentrates only on one or two specific plant foliar diseases and hence cannot be generalized for other diseases. Our research is oriented to disease independent detection and capable of application, for rust, bacterial blight, brown spot, downy mildew, frog eye spot and sudden death syndrome.



2. Methods reported by Cui et al. [3] and [2] rely on the accuracy of separating the foreground (leaf) from background, manually using photo-shop. The part of interest is chosen as in rectangular shape, which essentially takes some background pixels causing an error while computing the entire act of healthy pixels contributed by some background pixels as well. Therefore, all the parameters are affected by this rectangular ROI. Due to human intervention for finding the ROI, the proposed method is not the truly automatic. Our method is targeted to find the ROI, which is exactly matched to the shape of leaf rewarding no contribution by background in healthy pixel counts and more accurate results could be expected. 3. The sensing devices used in earlier proposed methods [2, 3] as mainly CCD camera causing a cost to be borne by the end users, while the proposed research work relies on simpler low resolution color cameras (e.g. the Mobile-cam) hence the possibility of wide applicability. 4. The image segmentation method reported by Cui et al. [2] for segmenting the healthy and diseased classes of pixels relied on hue and saturation values of the pixels. While practicing this method, it was very hard to separate two categories by human eyes. The proposed method utilizes the color filtering stage for segmentation. The filtering is done along the red channel in an RGB color image containing only the leaf area. This case of segmentation provides more perceptual distinction between the sound and the diseased pixels. 5. The authors [2, 3] have used various parameters like RCI, LCI and RIA which mainly refer to the disease severity with the infected area or infected pixels in a region of interest. It is pointed out that, if the count of infected pixels is high then the severity of a disease is more. This pint is not always valid. The cases, when disease pixels are more concentrated (relatively larger area) rather than being distributed (pin pointed or smaller areas) then this situation may cause early disconnection of leaves from the stems (early death of leaves). The proposed work takes care for the size of diseased spots (varying shapes and sizes) also, in contrast to the earlier reported research work. Through the proposed research work, it has been reported a new parameter known as damage severity index (DSI). The DSI gives an idea of extent up to which a leaf under observation has been damaged by any foliar disease. The larger values of DSI indicating lesser life expectancy for the leaves under observation. Beside DSI two more parameters have been derived for early warning of disease level severity namely infection per region (IPR) and disease level parameter (DLP), which are hashed out in detail in section 4. Further, the paper has been organized with the following hierarchy: section 2 gives a summarized overview of various plant foliar diseases mainly responsible for yield loss. Block diagram and description of the adopted methodology and procedure, various performance evaluation metrics and its impacts, either being in use or proposed have been discussed in section 3. Various results and observations have been taken and elaborated in section 4. Finally, various results have been analyzed and discussed, and future work has been mentioned in section 5.

2 Plant foliar diseases All the plant foliar diseases come under the category of biotic stress, which is mainly caused by biological factors like weeds, insects and diseases [4]. Various soybean plant foliar diseases, which are responsible for the major portion of yield loss are surveyed from the reported literature and elaborated in the Table 1.


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Fig. 1 Block diagram of the proposed methodology

3 Adopted methodology and procedures The block diagram of the proposed methodology, consisting of many blocks has been shown in Fig. 1. This block diagram elaborates the complete process for Automatic Soybean Plant Foliar disease detection and finding the evaluation parameters to decide the disease severity level. The entire methodology has been divided into many subsections as mentioned underneath. 3.1 Color image sensing, data collection and color calibration The set of images in the form of real input data used in this research has been collected from agricultural field in Guna district of Madhya Pradesh, India. A set of 1,000 images, ranging from healthy to diseased have been captured from the soybean cultivation field with varying conditions. Images have been taken from the very beginning of the seed coat to the foliage stage with varying fields, plants, developmental stages (vegetative and reproductive). All pictures are captured by mobile phone camera with specifications: Samsung GT-S3770, 2MP, resolution 1600x1200 pixels, exposure time 1/1,756 s and without flash, in sunlight while white/light background has been used and stored in JPEG format with horizontal and vertical resolution 96dpi. The motivation behind the use of the mobile camera is its widespread availability, cheap cost and borne by the common masses or more specifically the accessibility to farmers. Nowadays it is very common to have a cell phone network worldwide giving the choice to use the mobile phone camera as an obvious choice as real time color image sensor. Some of the collected data (120 images) in the form of images have been shown in Fig. 2.



Fig. 2 Various soya-plant images under varying conditions

After collecting the color image data of the soya plant leaves the color calibration process is adopted to reduce the effect of illumination variation caused by the unpredictable sun. The color calibration considers the Y component, which is essentially the intensity (energy) of the color sensed image and any change in illumination will cause a change in R, G, B color components causing the modified intensity values and the results will be affected. To overcome, this problem a normalized intensity value is used rather than the original value. The normalized value Y ¼ Y =E½Y ; where the E[Y] is the mean value of the Ychannel. Besides the real collected data, some of the previously referred data have also been used for experimental validation of the proposed methodology which is provided on request by the authors of the reported literature [2, 3], which is shown in Table 4. 3.2 Background separation and preprocessing Let the original diseased color soya plant leaf be denoted as IM×N×3 with M rows, N columns and 3-channels namely R, G and B. After collecting the input color image the three channels consisting of one luminanceY and two chrominance channels ICb and ICr are extracted. The process is given by the following equation [9] 32 3 3 2 3 2 IY IR 65:481 128:553 24:966 16 4 ICb 5 ¼ 4 128 5 þ 4 −37:797 −74:203 112:000 5 4 IG 5 112:000 −93:786 −18:214 128 I Cr IB 2

ð1Þ


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After extracting Y, Cb,Cr Channels of R, G, B image, the leaf object is separated from the background, and following procedure has been adopted. 1 IY, the normalized version of the original intensity channel is segmented using the bi-level thresholding method while taking the threshold value of 170. The value of threshold has been decided on the basis of extensive image analysis methods. Let the segmented image having light background and dark leaf object (B/W leaf image be represented as B. B¼

0 1

Iy ðx; yÞ > 170 0≤ x≤M &0 ≤y≤N Iy ðx; yÞ ≤170 0≤x ≤M &0 ≤ y ≤ N

ð2Þ

2 After segmentation few background pixels contribute falsely to the count of segmented leaf area pixels. These pixels are treated as noise and are removed by using opening followed by closing morphological filtering operations as described in (3). The structuring element used for morphological operation be is disk-shaped which is denoted by S1. IS1 ⇐B⊖S1 IS 2 ⇐IS 1 ⊕S1

ð3Þ

3 IS1 is the inverted version of previously processed image IS 1 , which will contain clear cut demarcation between white/ bright leaf clear object and dark/black background. After separating leaves from background further processing is required to get the infected region from the actual area of interest. The entire process is represented by 4 (a)–(c). IY∼ ¼ Iy ⊗ IS1 ICb∼ ¼ ICb ⊗IS1 ICr∼ ¼ ICr ⊗IS1

ð4aÞ

9 IR0 ¼ f IY0 ; ICb0 ; ICr0 = IG0 ¼ f IY0 ; ICb0 ; ICr0 ; IB0 ¼ f IY0 ; ICb0 ; ICr0

ð4bÞ

9 IR} ¼ IR ; ⊗IS1 = IG} ¼ IG ; ⊗IS1 ; IB} ¼ IB ; ⊗IS1

ð4cÞ

Where the ⊗ is pixel-by-pixel AND operator. The function “f” represents the dependence of the desired R, G, B channels on Y, Cb and Cr channels. The R, G, B channels are now modified and concatenated to get a color image of soya-plant without background, which is represented as lb. 3.3 Infected region determination The diseases discussed in this research paper are having yellow to dark-brown color distribution. From chromaticity diagram, it is quite evident that the above color distribution contains



the red component surely, but the other components, e.g. green and blue may or may not be present. This property leads to a color filtering operation to be applied to the image obtained after the step-3 in section 3.2 along the red channel. The red shaded pixels (components) are retained whereas the rest of the pixels are inverted to black. The final segmented image is having only the red shaded pixels. These pixels are highlighted to get the segmented image of infected soya-plant leaf. This segmented image (If1) is of bi-level, where the white portion indicating the infected region and black portion consisting of healthy, as well as, the background region pixels. Various bi-level morphological operations, namely opening and closing are applied to the final segmented image to get the smooth area of infected region. The shape of the structuring element S2 is of disk type. The equations (5) show the morphological operations to be applied on the segmented image (If1), I f 2 ¼ I f 1 ⊖S2 ð5Þ I f ¼ I f 2 ⊕ S2 Where, If Is the desired image having only two levels, namely black and white. Various parameters describing the soya-plant foliar disease severity, are calculated using IS1 and If for calculating total area and infected area of leaf respectively, under observation, where A, IA and HA represent the total area, infected area and Healthy area of leaf respectively. The entire process from capturing color leaf to healthy/infected area separation and critical parameter evaluation has been elaborated and well supported by the pictorial outcomes of various steps as shown in Fig. 3. 3.4 Disease detection parameters Many parameters have been used for quantification of plant foliar disease’s severity in the presented work and are elaborated in the following sub-sections, (a) Ratio of Infected Area (RIA): Ratio of the infected area is indicated in percentage of pixels which are infected with any plant foliar disease as proposed in literatures [2, 3].

Fig. 3 Process diagram for the proposed methodology (Top-row, left to right: original infected soya plant leaf, background separated image, filtered, inverted and YCbCr leaf image) (Bottom-row, right to left: RGB leaf image, RGB image with dark background, infected area detection, processing and calculations)


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The RIA is calculated as follows, RIA ¼

∑x ∑y 1ðx; yÞ 1A ¼ ∑x ∑y 1ðx; yÞ þ ∑x ∑y Hðx; yÞ 1A þ HA

ð6Þ

Where IA and HA represent the infected and healthy areas of the plant leaves respectively, which are the pixel counts for the region of interest using regionprops in MATLAB. I(x,y)andH(x,y) are the infected and healthy pixels for the respective regions after processing the soya plant leaf following the algorithm in proposed methodology. The value of RIA varies between 0 and 1 while more value indicating more disease severity. (b) Lesion Color Index (LCI): The lesion color distribution provides the basis for the subjective evaluation of the disease severity level. To quantify the disease severity level objectively the R-G color distribution is used to find the Lesion color index (LCI). LCI is evaluated on the basis of red and green color values of different pixels. The R-G based LCI is an effective indicator for all the diseases on green plant leaves as considered in this paper. Lesion color for the diseased pixels ranges between yellow and dark-brown. From the reported literature [3] the color of pixels will be perceived green if (R−G) 0 ∀LCIk ≤ 0

ð8Þ

As mentioned in the papers proposed by Cui et al. [3] and [2], the value of RSI is directly proportional to disease severity level. (c) Damage Severity Index (DSI): It has been observed that RIA and RSI are based on the total infected area or in other words, total infected pixels for the image under observation while being used for disease severity quantification. Obviously higher count of infected pixels is the direct indication for severity of the disease, which is not the case always because diseased spots may be distributed or concentrated. The large gross infected area will be defined either by a large number of small infected areas distributed apart or a small number of largely infected areas concentrated in their vicinity. The RIA and LCI only take care about the first case, while the second case is untouched, and on the basis of subjective observation, the disease may be more severe even if there are only few diseased lesions but contributing significantly in diseased or infected leaf area. Presented in this work a new subjective disease severity indicator has been derived and proposed, which is known as damage severity index (DSI) given as follows, DSI ¼

maxðAi Þni¼0 ∑x ∑y I ðx; yÞ

ð9Þ



∑ x ∑ yI(x,y)=∑ nAi and 1≤i≤n where n is the total number of diseased regions in the infected soya plant leaf.

4 Observation, result and discussion Several experiments have been conducted on the real leaf data collected from soybean field in Guna district of India between July and September 2012. Regardless of huge data set the results include only 120 images. These images are shown in Fig. 2. The Table 2 shows the

Table 2 Various disease severity indices and evaluation parameters Name of Total infected Total infected area Total leaf RIA(%) image regions (pixels) area (pixels)

DSI(%)

IPR

aa

31

57,690

414,699

13.906

12.894

18.609677 15

LL

ab

40

93,023

724,933

12.831

10.783

23.25575

LL

ac

35

35,638

497,147

7.168

12.169

10.182286 9

LL

ad

31

57,248

412,197

13.883

12.966

18.467097 15

LL

ae

25

252,958

588,733

42.9665 36.892

60

EHL

af

34

302,089

629,076

48.021

82.0877 88.849706 72

EHL

ag

43

58,173

656,628

8.859

23.2926 13.528605 15

LL

ah ai

37 85

108,218 151,044

706,443 65,750

15.3187 78.03 22.984 54.982

29.248108 40 17.769882 31

ML BMLL

aj

39

223,482

624,118

35.8

37.952

57.303077 43

ML

ak

45

298,176

702,263

42.459

91.857

66.261333 66

EHL

al

92

156,748

673,373

23.278

17.407

17.037826 19

LL

am

65

99,479

665,899

14.939

45.459

15.304462 25

BML

an

89

300,181

825,034

36.384

45.359

33.728202 38

ao

24

80,417

1,104,910

22.6227 18.7995 33.507083 24

BMLL

ap aq

32 44

283,891 274,382

644,442 591,572

44.052 46.381

EHL EHL

ar

34

262,700

733,909

35.7943 41.046

77.264706 51

HL

as

49

162,886

618,339

26.342

25.905

33.242041 28

BMLL

at

34

50,286

538,528

9.337

12.069

14.79

LL

au

39

101,396

456,898

22.1923 24.967

25.998974 24

BMLL

av

30

45,563

210,100

14.693

32.125

15.187667 20

LL

aw

31

21,215

673,692

3.0673

26.222

6.8435484 12

LL

ax ay

23 32

61,420 167,095

316,183 539,493

19.42 35.893

43.168 26.704348 29 59.5248 52.217188 49

az

40

94,523

753,058

12.9479 10.6704 23.63075

ba

32

121,887

578,435

22.3126 33.9021 38.089688 31

BMLL

bb

13

181,578

537,491

33.782

EHL

59.799 91.213

68.559

101.1832

DLP Disease level

15

88.715938 64 62.359546 66

12

15

139.67539 80

ML

BMLL HL LL

bc

27

36,101

720,287

5.29

20.3716 13.370741 13

LL

bd

18

50,164

584,933

9.1813

43.5142 27.868889 26

BMLL


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corresponding parameters for thirty images. The images are having the indifferent disease severity levels. From the Fig. 2 it can be seen that all the 30 leaves aa to bd are having all together different infected regions, infected area and appearance. The RIA and DSI and other values are shown in Table 2 for only 30 images because of limited space. A supplementary table is also given with the paper for entire 120 images. The leaf numbered bb and al are showing the minimum and maximum infected regions of 13 and 92 respectively. Similarly the minimum and maximum values of the total infected area, RIA and DSI are (36101, 302089) (3.0673, 48.021) and (10.553, 91.857) respectively for different leaves. From Table 1 it is evident that there is no correlation among the total infected region, total infected area, RIA and DSI. From Table 1 the value of RIA for the leaf images af and aq as shown in Fig. 3 are 48.021 and 46.381 respectively. For both the leaves, the values of RIA are quite high and supported by the respective value of DSI. The high value of RIA and DSI simultaneously shows those major portions of such leaves are infected while the spot size having a maximum area among all the diseased spots is quite high. Similarly, for leaf ah the value of RIA is 15.3187 and DSI 78.03 indicating a very large spot present in an infected leaf area which is also an alarming situation, while the only RIA values indicate less severe conditions for the same, which is not true. Again the value of RIA for leaves al and ao are 23.278 and 22.6277 respectively, while the corresponding values of DSI are in the range of 17 and 18, which is lower than that of RIA values. For al and ao the disease is distributed rather than concentrated. So from Fig. 2 and Table 2, it can be observed that either only RIA or DSI is not able to explain the level of severity quantitatively. The proposed DSI also indicates the density of the diseased spots. While considering the plant foliar diseases, there is a possibility of having very few spots but contributing more in infected areas and causing the most dangerous situation. This situation requires to be addressed properly. Thus, the total count of diseased spots is also an important criterion while deciding the disease severity level and giving a scope for formulating a new parameter, which is known as infection per region (IPR) as given in [10].

IPR ¼

Total infected Area 100 Total infected Regions

ð10aÞ

∑x ∑y I ðx; yÞ 100∑ni¼1 At

ð10bÞ

IPR ¼

From [10] it is decided that the IPR is directly proportional to the average size of diseased spots, irrespective of the number of spots present in the infected leaf. Thus, for a healthy leaf the value of IPR should be as small as possible and vice versa for the diseased leaf. Also, through the proposed research work an attempt has been made to quantify the disease severity level and also a parameter has been derived from it, which is known as a disease-level parameter (DLP) and obtained using (11),

DLP ¼ ω1 RIA þ ω2 DSI þ ω3 IPR

ð11Þ



Where ω1 =ω2 =ω3 are the weight factor and governed by (12), ∑3i¼1 ωt ¼ 1

ð12Þ

On the basis of DLP thresholds, the diseased leaf is classified as extremely-high-level (EHL), high-level (HL), medium-level (ML), between-medium-and-low-level (BMLL), lowlevel (LL) and almost-nil-level (ANL) respectively. This DLP threshold for any diseased leaf under observation is calculated using (13), DLPTH ¼

1 ðT hRIA þ T hDSI þ T hIPR Þ 3

ð13Þ

Where ThRIA,DSI&IPR are the threshold values for RIA DSI and IPR respectively, and determined assuming the Gaussian distribution for RIA, DSI and IPR for large diseased soya leaf data. Only 30 soybean infected leaves are used in our experiments for modeling the value of DLP’s. The threshold values are given as follows, 9 T hRIA ¼ μRIA j k 1 σ2RIA = ð14Þ T hDSI ¼ μDSI þ k 2 σ2DSI ; T hIPR ¼ μIPR þ k 3 σ2IPR Where μ and σ2 are respective mean and standard deviation for RIA, DSI and IPR and k1 = k2 =k3 =k are the constants and selected as 1.0, 0.5, 0.0, −0.5, 1.0 and −1.5 for classifying the leaf under observation pointing to the category of EHL, HL, ML, BMLL, LL and ANL respectively. From (13) and (14) the final value of DLPTh is evaluated as in (15), $ % 1 2 ∑μ þ k∑σ DLPTh ¼ ð15Þ 3 RIA;DSI&IPR On the basis of DLPTh the disease level has been classified as in Table 3. DLP' S In various diseased soybean plant leaves have been found and shown in Table 4 and 5. Now only on the basis of DLP value, the proposed system is predicting the disease severity level objectively for various leaf set from Fig. 3 which can be subjectively verified. Table 4 provides the visual representation of some of the infected leaves from the database collected, total number of diseased spots, total number of diseased regions in the form of continuous as well as discrete graphical representations versus areas of respective regions. RIA, DSI, IPR and DLP values have been calculated using the proposed methodology for three test leaves selected in the way that the disease level is varied and can be pictorially verified. If the performance is evaluated subjectively, then it can be seen that, the middle leaf is the most infected, whereas the upper and lower leaves are slightly less infected. The evaluation Table 3 Level cllasification

Disease level

DLPTh (Lower decision threshold)

EHL

57

HL

46

ML

34

BMLL LL

22 11

ANL

00


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Table 4 Parameter illustration

parameter RIA as suggested by earlier researcher is less than 50 %, but DSI value is quite high, more than 90 %. Higher the value of DSI indicating more damaged leaves. The values of the above mentioned parameters are the maximum in the case of the middle leaf and categorizing the leaf under EHL using the DLP threshold as shown in Table 3 whereas the top and bottom leaves are classified as under LL and BMLL classes. The comparison of efficiency and accuracy between the proposed method and the method reported by Cui et al. [3] and [2] is presented in Table 5. The LCI histograms of diseased images using the proposed method have been plotted. The zero value of LCI has been used as the reference line/threshold in both the cases. The value of LCI ranges between −1 and +1. The

Table 5 Comparision of the results for rust



distribution pattern for both the LCI histogram is similar in nature, conforming of the exactness of the proposed methodology. If the LCI histogram is concentrated towards the left of the reference line then it is the indication for less severe condition and as the histogram shifts towards right the severity level increases. Form the LCI histograms for top leaf in the Table 5 it can be seen that the proposed method is showing the more accurate, objective as well as subjective results compared to the existing methods. Also the effect of background has been removed in proposed method and only leaf area is taken as input for deciding the disease severity, contrary to the method proposed by Cui et al. [3] and [2]. Moreover the images supplied by Hartman to us are of lesser in size, low contrast, non-uniform format, particularly the middle one in Table 5 causing the difference between evaluated and reported value of RIA. Although the RIA value is less in case of middle image, but we are more concerned of DSI, IPR and DLP values. On the basis of the proposed parameters the top, middle and bottom leaves of the Table 5 can be classified as under EHL, LL and ANL classes respectively (out of five classes), whereas it has been classified in only three classes by Cui et al. [3] and [2]. So by using the proposed method the disease level is classified more accurately on the basis of DLP threshold. The efficiency of the proposed methodology has also been proven by comparing the results for another soya plant foliar disease, namely frog’s eye as shown in Table 6. Dorence et al., [6] has been reported the disease level on the basis of diagrammatic scale, which is evaluated manually. The diagrammatic scale is decided by the plant pathology experts in laboratory conditions and calculated by taking various soya plant leaves with varying disease severity level. The Table 6 shows the reported diagrammatic scale (in percentage) of 26, 15, 10 and 1

Table 6 Comparision of the results for frog’s eye


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respectively for various leaves affected by frog eye. The proposed methodology has been applied to the same leaf images as reported by Dorence et al. [6], and the proposed parameters have been calculated. The RIA values are shown the maximum to minimum for the leaves from top to bottom respectively, showing the maximum to the minimum infected area of the leaves similar to the diagrammatic scale as reported by Dorence et al., [6]. This confirms the possible use of the proposed method for frog’s eye disease. The DSI value is more in case of the top, and the bottom leaves revealing the fact that the disease is more severe in the case of the top and the bottom, whereas less severe for the two middle leaves, indicating the presence of only few but relatively bigger spots as a present infected leaf. The value of IPR also follows the pattern similar to the RIA, and diagrammatic scale, but revealing different information, which is the average infection per region. More value of IPR indicating a more dangerous situation. So the proposed method removes the requirement for calculating the diagrammatic scale for diseased leaves manually because the proposed method is fully automatic one. Despite the high DSI value as in the case of the lowest row the low IPR value conforms of the less dangerous situation. So with the help of the proposed method more accurate and timely prediction can be done while eliminating the need for expert plant pathologists. The proposed method does not require any expert or the use sophisticated laboratory.

5 Conclusion and future scope Through the present paper, we have tried to highlight the problems associated with the cultivation of soybean and causes of low yield loss in the developing countries like India. It has been takenup six soya plant foliar diseases, namely; Rust, Bacterial Blight, Sudden Death Syndrome, Brown Spot, Downy Mildew, and Frog Eye, which are mainly responsible for significant yield loss through the presented paper, it has been proposed a fully automatic method for identification of plant failure disease and also to classify the disease severity level using five classes. It has been derived and development various new parameters and indices like DSI, IPR, DLP, which are subsequently used for disease level prediction. The proposed methodology has been implemented successfully and performance tested on a real set of soya leaf data. The result reported in this paper are quite convincing and having the potential of widespread adoptability of the proposed method by the developing countries, where such information plays an important role for improvement in yield. The proposed method uses mobile cams for capturing the diseased images and does not require any kind of special training and sophisticated capturing devices. The proposed method is (i) fully automatic for ROI calculation, background separation and parameter evaluation (ii) disease independent; (iii) low cost and possibility for the wide usability in field conditions, (iv) simpler segmentation method and more advanced parameters are used. In the future, the proposed methodology can be integrated with other yet to be developed, methods for disease identification and classification using color and texture analysis to develop an expert system for early soya plant foliar disease warning and administration. In this expert system the disease type can be identified by color and texture analysis and the severity level estimation by our proposed method. Our proposed method is disease independent while considering the six most harmful diseases as in this paper. The performance of the system can be improved in the future by using advanced background separation methods to separate the leaf object from a complex background. Acknowledgments We want to acknowledge Prof. G. L. Heartman for providing the valuable suggestions, leaf data and technical expertise. Last but not the least we acknowledge Dr. Dean Malvick Assistant Professor and Extension Pathologist Department of Plant Pathology, University of Minnesota, (http://www.extension.umn.edu/ cropdiseases/soybean) for providing the permission to use the diseased soya leaves.



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Mr. Sourabh Shrivastava has completed his Bachelor of Engineering and Master of Technology from R.G.P.V. and M.A.N.I.T Bhopal in 2007 and 2010 respectively. At present he is pursuing Ph.D in Computer Science and Engineering Department, Jaypee University of Engineering and Technology Guna, Madhya Pradesh, India under the joint-supervision of Prof. Dhara Singh Hooda and Dr. Satish Kumar Singh from Jaypee university of Engineering and Technology Guna India.

Dr. Satish Kumar Singh has completed his B. Tech, M.Tech and PhD in 2003, 2005 and 2010 respectively. He is presently working as Assistant Professor in Indian Institute of Information Technology, Allahabad, India. He is having 10 years of academic and research experience. He is serving as editorial board member and reviewer for various national and international journals (Elsevier, Springer, Oxford, IET-IPR etc.). He is the member of various professional societies namely IEEE, IETE, IACSIT, IEANG etc.



Professor D. S. Hooda did his M.A. (Maths.) from Delhi University in 1969 and M.Phil. from Meerut University, Meerut, in 1976. He joined CCS Haryana Agricultural University, Hisar as a Lecturer in 1970 and was appointed Asst. Professor of mathematics in 1972. He did his Ph.D. from K.U. Kurukshetra in 1981. His field of specialization is Information Theory and its Applications and his research interests are information measures, source coding, entropy optimization principles and their applications in statistics, finance mathematics, survival analysis, and bounds on probabilities of error, pattern recognition, fuzzy sets and fuzzy information. He has published about 90 papers in various journals of National and International repute. He has authored 26 popular articles and 8 books in mathematics and statistics and one book on “Aryabhatta: life and contributions”.