chapter 5 image processing method for crop status...

Chapter 5

Image Processing Method for Crop

Status Management

This chapter discusses the concept of image processing algorithms and its implemen-

tation for crop status management issues for sugarcane crop. Primarily there are three

issues:

1. Measurement of growth

2. Estimation of the chlorophyll content and

3. Disease severity of the leaf

Section 5.1 discusses the background of sugarcane crop and further sections would

highlight technical details (Implementation and Results).

5.1 Introduction

The duration of sugarcane crop in India ranges from 10-18 months, a 12 months crop is

most common. Experimentally and by research it has been proved that for high yield

of the sugarcane, careful crop status management is essential during the germination

and tillring stages of the growth [2].

Soil quality, fertilizer and micronutrients, water and other environmental factors

such as temperature and humidity play an important role in the growth of the sug-

arcane. Compared to other crops, the cultivation of sugarcane demands more water

(150-200 times more than other crop like jawar). The application of fertilizers and

pesticides is also quite more [40]. Considering all these factors the productivity of

sugarcane crop draws attention of farmers.

53

The growth of sugarcane is measured by stalk and leaf dry weight in gram per

centimeter square but this destructive method is rarely used for growth measurement

[58]. Measurement of vertical height of the sugarcane is commonly used method in

practice to measure the growth. Traditionally, the plant geometrical parameters are

measured using tape and protector [59]. Though these measuring tools are simple, the

procedures are difficult and also results are not accurate.

The plant leaf colour is again commonly used tool to specify health status of the

plant. Chlorophyll is a green pigment found in almost all plants, which allows the

plants to obtain energy from light [20]. The loss of chlorophyll content in leaves oc-

curs due to nutrient imbalance, excessive use of pesticide, environmental changes and

ageing. Various kinds of colour plates are available for estimation of chlorophyll con-

tent of plants [60]. Chlorophyll meter (SPAD), have been developed to estimate leaf

chlorophyll content [23]. These tools are a good option to chemical analysis method

and remote sensing method used to find chlorophyll content of the plants [61].

Most of these techniques are quite accurate but they are rarely used in practice

because of high cost of SPAD meter, unavailability of remote sensing system and other

constraints.

Another issue, concerning the sugarcane agriculture management is monitoring and

controlling of the diseases. For example fungi-caused diseases in sugarcane are the most

predominant which appear as spots on the leaves. These spots prevent the vital process

of photosynthesis and hence, to a large extent it affects the growth of the plant and

consequently the yield. In case of severe infection, the leaf gets totally covered with

spots impacting a loss of yield [31].

One may suggest the use of pesticides but excessive use of pesticide against the

protection of disease, however, increases the cost of production and results in environ-

mental degradation [62]. This drawback can be removed by estimating severity of the

disease and targeting the diseased places, with the appropriate quantity and concen-

tration of pesticide.

Traditionally, the naked eye observation method is used in the production practices

to measure the severity of disease. However, the results are subjective and are not

possibly used to measure the disease precisely [26]. Grid counting method can be used

to improve the accuracy of estimation but it is a cumbersome and time consuming

procedure.

54

Figure 5.1: Details of sugarcane plant

Therefore, there is need of effective technique for growth measurement, chlorophyll

measurement and disease severity measurement which must be a cost effective and ac-

curate support for the sugarcane agriculture management. How to use image processing

algorithms to solve the issues discussed so far in next subsequent sections.

5.2 Growth Measurement

In sugarcane, a standard way of growth measurement is to measure the length between

the +1 dewlap and lower end of a main stem [63]. Where, +1 dewlap is nothing but a

fully developed leaf as shown in Figure 5.1. The dewlap acts as a hinge between the

blade and leaf sheath. Similarly, the developments of growth of sugarcane are referred

as +1 dewlap, second +2 dewlaps and so on. Undeveloped leaves, higher than the +1

leaf are called the zero dewlaps. During the early days of the sugarcane growth, the

length of the leaf sheath grows rapidly. The apparent growth of the sugarcane is the

sum of true growth along with the growth of the leaf sheath [64].

In this understanding, the length between the first dewlap and the lower end of a

main stem is considered to be an indicator of the sugarcane growth.

5.2.1 Analysis and Design

During the experimentation, three good quality eye buds of sugarcane (Sugarcane seed

type: Cultivar Co-86032) were planted in the pots (on the 1st June 2010). Soil se-

lected in pot was lumpy soil whose contents are N-164.25, P-5.38, K-69.44, Cu-0.16,

Fe-2.68, Mn-1.24, Zn-0.12. The pots were kept in open space (North-East side) so that

they got maximum sunlight. Images of the plant were captured in interval of five days

55

Figure 5.2: Sugarcane plant structures with 0 and +1 dewlap

up to 3 months and at the same time growth was measured physically using meter scale.

It was observed that growth of stem was in the straight direction and leaves were

spreading in several way alternatively. Growing structures were also observed to be

different for individual plants of sugarcane. One can easily distinguish the position

of the dewlaps in an image as the eyes are able to recognize the boundary shape and

colour features of the plants. To measure the growth using image processing algorithm

it is necessary to distinguish the distinctive boundary shape and colour features of

sugarcane plants [65]. Certain features which are considered are as follows:

1. Stem: Shape- Straight, Colour- Red, Green,

2. Leaf blade: Shape-Curve gently, Colour- Green,

3. Tip of leaf blade: Shape- Acute angle, Colour- Green,

4. Dewlap: Shape- Obtuse angle at the joint of a leaf blade and a leaf sheath,

Colour - Green.

Since the structure of each plant is different with respect to its degree of growth.

There are following three classifications of cases of plant structure:

Case 1 : Those plants which have only one leaf, is an undeveloped leaf. Such plants

are discarded from measurement procedure. Pictorial understanding of this pos-

sibility is shown in Figure 5.2(a).

Case 2 : If there is one leaf axil seen from the stem edge and the other leaf is unde-

veloped along the stem, there could be two possibilities:

56

Figure 5.3: Sugarcane plant structures with +2 dewlaps

1. If the slope of left leaf is gentler than undeveloped leaf, then left leaf is +1

dewlap and undeveloped leaf is 0 leaf.

2. If the slope of right leaf is gentler than the undeveloped leaf, then the right

leaf is +1 dewlap and undeveloped leaf is 0 leaf. Pictorial understanding of

these two possibilities is shown in Figure 5.2(b),(c).

Case 3 : If there are two leaf axils seen from the stem edge and undeveloped leaf along

the stem, then +1 dewlap is at left, +2 dewlap is at right and 0 leaf is along the

stem, as shown in Figure 5.3(a),(b).

First fully developed leaf: This is the +1 leaf of sugarcane. As per expert’s

suggestions, a leaf is termed as first fully developed leaf if angle between A and B of

plant models is ≥ 200

i.e.∠AOB = φ ≥ 200 (5.1)

Where,

A - Leaf blade boundary,

B - Stem boundary,

O- Joining point of A and B.

Thus, the concept of measuring the growth of the sugarcane plant lies between the

lower ends of the stem to +1 dewlap.

57

5.2.2 Implementation

In this research, there is a sequence of steps to be followed in order to correctly measure

the growth of the plant.

Algorithm:

1. Acquire the image

2. Resize the image and convert to proper format

3. Segment the image (Convert to digital image)

4. Based on the segmented image, measure the properties of image

5. Compute the reference height and reference factor from these properties

6. For measurement of height:

(a) Compute the properties of segmented image

(b) Smooth the image to trace object from bottom of the image

(c) Find all left and right branches

(d) Compute the angle of interaction and decide whether it is a branch

(e) When the last branch is reached, count the number of pixels from first point

to end point (last branch)

(f) Calculate growth of the plant by using reference factor calculation step.

A short description of the process and steps involved are:

1. Image acquisition : Digital camera specification- Make Nikon, 12 Mega pixels,

4X zoom. A digital camera of above specification was used to acquire images.

For proper visibility of plant and easy analysis, following terms were considered.

(a) Background: In the young stages of the plant, leaf colour is green and stem

colour shed ranges from Red to Green. In this case white background is

used for accurate segmentation.

(b) Standard reference: A 90 × 10 mm size brown strip is fixed on white back-

ground which is used as standard reference in growth measurement.

(c) Adjustable position: The distance between camera and pot is adjusted in

such a way that camera covers the full area of stem from lower end to top

end along with brown strip in an image.

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Figure 5.4: Original image of sugarcane plant

(d) Environment: All images are captured in cool white fluorescent light.The

sample image of sugarcane plant is shown in Figure 5.4.

2. Image re-sizing : A high resolution camera was used for acquisition of colour

image size 4000×3000, the image is down sampled by factor of 10 (resized to

400× 300), for low processing time. The resizing of an image was performed by

the process of the interpolation [44].

3. RGB to grayscale conversion : Input image is first converted to the grayscale

intensity image. The formula used to covert input colour image to gray scale is

given in equation (5.2).

I = 0.3R + 0.59G+ 0.11B (5.2)

Image is captured in constant cool white fluorescent light placing the plant in

front of the white background, results into a large difference in between gray

values of two groups, object and background is as shown in Figure 5.5.

Figure 5.5: Gray scale image of sugarcane plant

59

4. Image segmentation : Image segmentation is the important step to separate

the different regions with special significance in the image, these regions should

not intersect with each other and each region should meet the consistency con-

ditions in specific regions [66]. In this experiment standard reference and plant

is separated from the white background.

• Segmentation technique: From the image statistics, maximum and minimum

gray levels are obtained and threshold value is computed by averaging of

these levels [67]. This threshold value converts the gray scale image to a

binary image. The thresholding value for sample image comes out to be 91

and the resultant binary image is as shown in Figure 5.6.

Figure 5.6: Binary image of sugarcane plant

5. Image filtering :The binary image is negated and morphological operation

(close and fill) is used to filter the image. Morphological operations are based on

relations of two sets. One is an image and the second is a small probe, called a

structuring element. The structuring element systematically traverses the image

and its relation to the image in each position is stored in the output image [47].

Closing tends to smooth sections of contour; it generally fuses narrow breaks and

the long thin gulf, eliminates small holes and fills gaps in contour. The closing

of set A by structuring element B , denoted A •B is defined as :

A •B = (A⊕B) Θ B (5.3)

Closing might result in amalgamations of disconnected components, which gen-

erated new holes. Region filling had to be adapted. The algorithms for region

filling based on set dilation, complementation, and intersections, as shown in

Equation 5.4

Xk = (Xk−1 ⊕B) ∩ AC k = 1, 2, 3, ... (5.4)

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Where, A is a set of boundary and B is structuring element. The algorithms

terminates at iteration step k if Xk = Xk−1.

In the sample image during eroding operation lower leaf is removed from the

image because of very thin edge.

6. Computation of reference factor : The binary image includes reference ob-

ject and plant. To calculate the reference factor it is necessary to separate the

standard reference from the image. The image consists of two labeled regions

corresponding to plant and reference object. Objects labeled image is shown in

Figure 5.7.

Figure 5.7: Plant and reference object labeled image

From labeled image the area of reference object is computed. In sample labeled

image, area of reference object is 269 pixels, therefore the reference object is

separated from plant object assuming area less than 300, and greater than 30

pixels to remove unwanted pixels in the image.

• Reference factor is calculated as

Reference Height(Hr) = Max rowsize−Min rowsize (5.5)

In sample image maximum and minimum row size is 62 and 03 respectively.

Hence,

Reference Factor(Rf ) =Standard reference height

Hr

(5.6)

In sample image Hr = 59 and Standard reference height = 90mm. Thus,

Rf = 1.5254 This value will be required to calculate the actual growth of the

plant at the end.

61

Figure 5.8: Cropped image of plant

7. Computation of growth of sugarcane plant : For the sample plant shown in

Figure 5.7, pixels corresponding to the reference object are less than 300. After

morphological filtering, based on the black pixels from top to bottom, the cropped

image is as shown in Figure 5.8. This extracted area is considered as the dewlap

search region.

• Extraction of a dewlap : Dewlaps are searched on the assumption that

they appear on the stem edge in alternate position. It is difficult to search

the edge of the stem if withered leaves exist in an image, so withered leaf is

eliminated before the dewlap detection.

The boundary shape of the main stem is almost straight in each plant.

Clockwise boundary tracking is performed on the dewlap search area [68].

For the binary image, all white pixels belong to the object and all black

pixels constitute the background as shown in Figure 5.9

Figure 5.9: Boundary tracking

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The y-coordinate of the start point was the same as the y-coordinate of the

lower end of the stem. Numbers from the start n, called boundary numbers,

and coordinates (Xp(n), Yp(n)) were recorded.

Chain coding expressing the moving direction of adjacent pixels on a bound-

ary is one way to provide for shape recognition. Since the chain pixels are

limited to -180,-135,-90,-45, 0, 45, 90 and 135 degrees.

Calculated boundary curvature which was defined by an exterior angle

formed with the present boundary pixel and pixels 20 pixels apart from

it in the backward and forward directions to increase the angular resolu-

tion. The boundary curvature φ(n) was calculated and recorded for all the

boundary pixels.

Figure 5.10 shows the curvatures at a dewlap, the tip of a leaf and an axial.

Figure 5.10: Boundary tracking in both directions

After tracing in both directions (right and left) and obtaining the bound-

ary curvature, we get exterior angle [69]. The leaf tips or axils are clearly

detected from the boundary curvature, therefore the axils that correspond

to the dewlap are detected first and then dewlaps are determined based on

the locations of the axils [70].

The boundary pixels that satisfy the following inequalities are confirmed as

candidates for axils.

Referring Figure 5.11, ∠AOB = φ ≥ 200

63

Figure 5.11: Sugarcane plant structure with +1 dewlap

Where,

A = leaf blade boundary,

B = Stem boundary and

φ=angle between A and B

If two or more pixels satisfy this inequality at one axil, the nearest one to

the low end of the stem is selected as the true axis. The detected axils are

searched by the distance from the lower end of the stem [71, 72]. The pair

(Xai, Y ai) consists of the x and y coordinates of the ith highest axial. The

objective of this process is to detect the dewlap of the +1 leaf. However the

structure of each shoot differed according to its degree of growth and facing

direction.

In sample image the structure of sugarcane plant is similar to case 3 that

described earlier in different growth structures of plant. There are two or

more axils. The boundary number of the highest axial p and that of the

second highest one q is compared. If p is smaller than q, the left side of the

shoot is searched.

Here, it is assumed that if angle φ is greater than or equal to 200 it is a fully

developed leaf.

Angle φ is calculated as below:

64

φ = aCosdpA.B

×180

π(5.7)

Where,

dp= Dot product of the vectors,

A and B = Length of the vectors and

aCos = Inverse cosine results in radians.

• Computation of growth of plant : To detect the developed leaf and its

angles, stem is traced in bottom up fashion. The top most leaf blade with

angle φ ≥ 200 and the highest distance from the lower end is the +1 dewlap

[73].

The distance of +1 dewlap from the lower end is the growth of the plant.

i.e. L = (Emax ×Rf )× Averagingfactor (5.8)

Where,

L = Growth of the plant in mm,

Emax= Maximum pixel count from lower end (as computed),

Rf= Reference factor (as computed).

Averaging factor = 0.86 (Since the pot and standard reference are not in

the same plane, we consider this factor 0.86; value is obtained after iterative

evaluation over 50 images).

For the sample image,

Emax = 47 and

Rf = 1.5254

Thus,

L = 61.65mm

The +1 dewlap detected sample image is as shown in Figure 5.12.

5.2.3 Results and Discussions

In sugarcane agriculture it is essential to monitor the growth during the first two stages

that is Germination and Till-ring for efficient fertilization and irrigation purpose. The

65

Figure 5.12: +1 dewlap detected image of plant

approximate period of these growth stages is of 120 days.

In this research the vertical growth of sugarcane plants, planted in pots is measured

by traditional method of measurement that is by using meter scale under the guidance

of sugarcane agricultural scientist. These readings are considered as standard measure-

ments (SM) for validation of results of designed algorithm for growth measurement.

For experimentation purpose, 72 sample images of sugarcane plants are tested using

the designed algorithm. Tabular representation of measurement of growth of these 72

samples is given in Appendix-C. The objective of this algorithm was to measure the

growth (i. e. the length of the +1 dewlap from the lower end of stem).

Accuracy of measurement of growth by designed algorithm is calculated by referring

the readings of standard measurements. Equation 5.9 and 5.10 gives Deviation Factor

(DF) and Accuracy (AC) as:

DF = (SM − EM)×100

SM(5.9)

AC = 100−DF (5.10)

Where,

DF - Deviation Factor,

AC - Accuracy,

SM - Standard measurement (measurement by manual method) and

EM - Experimental measurement (measurement by Algorithm)

The accuracy of measurement of growth by proposed method comes out to be 96%.

A comparison of growth measured by the proposed method and growth measured by

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0 10 20 30 40 50 600

10

20

30

40

50

60

Growth by meter scale

Gro

wth

by

prop

osed

alg

orit

hm

Correlation between growth measurement

Plant 01 RMSE = 0.2872

Figure 5.13: Correlation between growth measured by standard and experimental mea-

surement method

meter scale is shown in Figure 5.13. The Root Mean Square Error (RMSE) of com-

puted values with respect to meter scale values is 0.2872. The overall result indicates

that there is a good agreement between growth measured by meter scale and growth

measured by proposed algorithm.

5.3 Chlorophyll Measurement

Chlorophyll is a green pigment which is the basic ingredient of the leaf of a plant [74].

It is due to the presence of chlorophyll, that the leaves are green colours in nature.

Chlorophyll absorbs certain wavelengths of light within the spectrum of visible light as

shown in Figure 5.14. It absorbs both red region (long wavelength) and the blue region

(short wavelength) of the visible light spectrum while reflect green colour wavelength

which makes the plant appear to be green [75, 76].

Plants are able to satisfy their energy requirements by absorbing light from the blue

and red parts of the spectrum. However, there is still a large spectral region between

500 to 600 nm where chlorophyll absorbs little amount of light.

67

Figure 5.14: Absorption spectra of chlorophyll

Chlorophyll measuring meters measure the optical absorption of a leaf to estimate

its chlorophyll content [77]. Chlorophyll molecules absorb in the blue and red bands,

but not the green and infra-red bands, thus meters measure the amount of absorption

at these bands to estimate the amount of chlorophyll present in the leaf.


From the frequency spectra of absorption of chlorophyll, it can be seen that there

is good relation between chlorophyll content of leaf and primary colours light (Red,

Green, and Blue).

The intensity of colour for each leaf disk is an arbitrary unit of intensity called

inverse integrated gray value per pixel [26]. It is proportional to the actual concentra-

tion of chlorophyll per pixel. Thus the concentration of chlorophyll per pixel can be

measured from the colour image of leaf.

The chlorophyll value of sugarcane leaf can be measured by chlorophyll meter but

it tends to be costlier [25]. It also suffers from another limitation that the readings

cannot be taken at low light intensity.

Thus there is a need of a cost effective system which can overcome the above limi-

tation.

Hence an algorithm is designed to measure the chlorophyll content of the sugarcane

leaf using RGB based image analysis. Presumably, any colour can be decomposed into

the primary colour components (RGB) and the intensity of an individual colour upto

some extent can be represented by brightness in a digital image [78]. Thus it is pos-

68

sible to develop a mathematical correlation between chlorophyll content of the leaf of

sugarcane plant and the brightness values of the primary colours.

For the purpose of verification of results, the chlorophyll value of sugarcane leaf

was measured as SPAD (Soil Plant Analysis Development) values with the help of

chlorophyll meter (CM 1000 meter manufactured by Spectrum Technology USA). The

chlorophyll meter measure transmission of Red light (650nm) at which chlorophyll

absorbs light, as well as transmission of Infrared light at 940 nm, where no absorption

occurs. The instrument calculates a SPAD value based on these two transmission values

and it is well correlated with chlorophyll content. The chlorophyll meter calculates the

SPAD values (M) by means of the Equation 5.11.

M = log[(I ′940/I940)/(I′

650/I650)] = log[(I ′940/I650)/(I′

650/I940)] (5.11)

An exponential relationship exists between the SPAD value and chlorophyll content.

The leaf chlorophyll meter output can easily be converted into the amount of leaf

chlorophyll content (µmolm−2) by the exponential equation 10M0.261, where M rep-

resents the meter output. The meter readings are considered as a standard measured

value.

Proposed image processing based method of chlorophyll measurement:

Taking into consideration, the targeted area of leaf by the Chlorophyll meter (CMY

1000) during measurement, the leaf was cut to 6× 2 cm size and the image of leaf was

captured by digital camera. From this image of leaf, mean brightness of primary colour

(R, G, and B) were recorded. The colour components were processed separately and

data were sorted based on the colour distribution as shown in Figure 5.15.

Figure 5.15: The histogram of the brightness values of the primary colours

69

The histogram represented the number of pixels occurring at each colour image

with a particular brightness value. The mean brightness values of the three- colour

components with standard deviations of a sample were obtained from the histogram

and are presented in Table 5.1.

Table 5.1: Mean brightness value with standard deviation and Coefficient of variation

Description Red Green Blue

Mean brightness value 138.02±3.60 145.88 ± 2.88 48.14± 3.82

Coefficient of variation 50 43 86.00

The small coefficients of variations for three primary colours indicated that the

mean brightness values were highly reproducible.

The mean brightness values of the three primary colours were linearly correlated to

obtain the characteristics RGB model as below

Y = aR + bG + cB (5.12)

Where, Y is predicated chlorophyll,

R, G and B are basic primary colours and

a, b and c are model parameters

In the next step these primary colours were transformed into spectral parameters

such as Hue (H), Saturation (S) and Value (V) and the chlorophyll content of the leaf

is obtained as in [78]

Y = aH + bS + cV (5.13)

Where,

Y is predicated chlorophyll,

H, S and V are spectral parameters of basic primary colours and

a, b and c are model parameters

Model parameters a, b and c are evaluated using regressive method (discussed in

detail in further section).

Thus the proposed system is designed for measurement of chlorophyll content of

sugarcane leaf that involves:

1. Operation of capturing of image

70

2. Computation of mean H, S and V values and

3. Estimation of chlorophyll content of leaf.


In this research an algorithm is designed to measure the chlorophyll content of the

sugarcane leaves.

Algorithm:

1. Acquire the image of leaf

2. Preprocessing of image to convert in proper format

• Image resizing

• RGB to HSV conversion

3. Segmentation of leaf region

• Smoothing the image

4. Feature extraction

• Compute the model parameters a, b and c

• Estimate the chlorophyll value by formula

1. Acquiring the images : Following experimental set-up was made for image

acquisition:

(a) Digital camera: 12 Mega pixel, 4X zoom (Nikon make) was used for acquir-

ing the image.

(b) Background selection: Selection of background is an important step to dis-

tinguish object from background. Magenta colour is complementary to green

and therefore provides good colour contrast, making hue separation success-

ful, but sharp edge between magenta and green area results in noise during

the background separation therefore white background was used [79].

(c) Sugarcane leaf was placed flat on white background in natural light. The

optical axis of digital camera was adjusted perpendicular to the leaf plane

to shoot images.

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2. Preprocessing of image to convert into proper format : To separate leaf

from the background, the following steps are followed.

• Re-size the image : Size of captured image is 4000×3000. The image is

down sampled by a factor of 10 (resized to 400×300), to improve the speed

of further process. The resizing of an image is performed by the process

of the interpolation [44]. This resized colour image is used for HSV space

conversion.

• RGB to HSV conversion : The hue (H) defines the colour according to

wavelength, while saturation (S) is the amount of colour. An object that

has a deep, rich tone has a high saturation and one that looks washed out

has a low saturation. The last component value (V) describes the amount

of light in the colour [80]. The main disadvantage of the RGB model is that

humans do not see colour as a mix of three primaries. Rather our vision

differentiate between hues with high or low saturation and intensity; which

makes the HSV colour model closer to the human perception than the RGB

model. In the HSV space it is easy to change the colour of an object in an

image and still retain variations in intensity and saturation such as shadows

and highlights. It is simply achieved by changing the hue component, which

would be impossible in the RGB space. This feature implied that the effects

of shading and lighting can be reduced [14].

Let r, g, b ∈ [0, 1] be the red, green, and blue coordinates, respectively,

of a colour in RGB space. Let maximum be the greatest of r, g and b and

minimum the least.

To find the hue angle, h ∈ [0, 360] for HSV space is computed as:

h =

0 if max = min(

600 × g−b

max−min+ 00

)

mod3600 if max = r

600 × b−rmax−min

+ 1200 if max = g

600 × r−g

max−min+ 2400 if max = b

(5.14)

The value of h is generally normalized to lie between 0 and 3600, and h = 0

is used when max = min (for gray values) though the hue has no geometric

meaning there, where the saturation is zero.

The values for s and v of an HSV colour are computed as:

72

Figure 5.16: Original image of sugarcane leaf

s =

0 if max = 0

max−minmax

= 1− minmax

otherwise(5.15)

v = max (5.16)

The range of HSV vector is a cube in the Cartesian coordinate system, but

since hue is really a cyclic property, with a cut at red, visualizations of these

spaces invariably involve hue circles. Cylindrical and conical depictions are

most popular, spherical depictions are also possible.

3. Segmentation of leaf region : The digital image of leaf grabbed by camera is

composed of leaf and background is shown in Figure 5.16. To obtain leaf feature,

first the leaf needed to be separated from background [81]. It is important to

choose a proper colour space for effective image segmentation.

In this experimentation, leaf segmentation is achieved by a threshold algorithm

with I1, I2 and I3 feature as follows:

I1(find(DH ≤ T1)) = 1 (5.17)

I2(find(DS ≤ T2)) = 1 (5.18)

I3(find(DV ≤ T3)) = 1 (5.19)

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Figure 5.17: RGB and HSV colour values

Where,

DH = Difference of absolute H value (i.e. 0.33 for green colour) and actual value

of leaf for green colour,

DS = Difference of absolute S value (i.e. 0.6 for green colour) and actual value


DV = Difference of absolute V value (i.e. 0.1 for green colour) and actual value


T1, T2 and T3 = Tolerances at positive side i.e. 0.15, 0.5, and 0.5 respectively.

I1, I2 and I3 = Colour space characters.

H,S and V constants are set from Figure 5.17.

Where,

I = I1.I2.I3 (5.20)

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Figure 5.18: Binary image of leaf

Here, I is binary image is shown in Figure 5.18, where white pixels correspond to

leaf region and black pixels correspond to background.

• Image smoothing : In second step, binary image of leaf is morphologically

processed to fill holes in the image. A hole is set of background pixels that

cannot be reached by filling in the background from the edge of the image.

Morphological process named closing (i.e. dilation followed by erosion) is

performed that tends to smooth sections of contours, it generally fuses nar-

row breaks and long thin gulf, eliminates and fills gaps in the contour, that

removes the noise pixels present in the leaf image [47]. Finally it bridges

unconnected pixels in the image.

In the third step, a new image is formed from the original image and binary

image of leaf that consist only leaf without background which includes cor-

responding R, G and B values of each white pixels from original leaf image.

Separated leaf image from background is shown in Figure 5.19. In this way

leaf is separated from background image and consumption of computing is

significantly reduced because the follow-up algorithm will only work on the

area of leaf [81].

4. Feature extraction : Feature extraction includes colour image processing steps

to measure the green colour purity to indicate the amount of chlorophyll present

in leaf. From this green image, the mean values of H, S and V space are computed.

• Computation of model parameters : Model parameters in Equation

(5.13) are determined by the least square method by using the individual

H, S and V values and chlorophyll (Y) measured by chlorophyll meter of

75

Figure 5.19: Leaf separated from the background

120 leaves. The parameters a, b and c were determined by the matrixes from

the H, S and V model as shown below

[a b c] = [ATHSV · AHSV ]

−1· AT

HSV · Y (5.21)

AHSV represents the mean spectral values and vector Y represented the

chlorophyll content of the leaves of plants determined by the chlorophyll

meter. The computed module parameters a, b and c values for sugarcane

leaves are 378.4381, 68.7543 and -216.1429 respectively.

• Estimation of chlorophyll value :Referring back to equation (5.13), the

chlorophyll of sugarcane leaf computed using HSV colour space as below:

Y = aH + bS + cV

In a sample image the spectral parameters are H, S and V are 0.2373,0.2935

and 0.234 respectively. Model parameters that have been already calculated

are a=378.438, b=68.754 and c=-216.142. Therefore the estimated chloro-

phyll value by proposed algorithm is 59.4 and chlorophyll value of the same

leaf measured by chlorophyll meter is 60.


The chlorophyll content of sugarcane leaves is predicted by using the HSV model.

The model parameters as determined by regressive method come out to be 378.4381,

68.7543 and -216.1429 respectively.

76

0 50 100 150 200 2500

50

100

150

200

250

Chlorophyll by meter

Chl

orop

hyll

by p

ropo

sed

algo

rith

m

Correlation between Chlorophyll measurement

RMSE = 1.9334

Figure 5.20: Correlation between actual chlorophyll and predicted chlorophyll

A comparison of estimated chlorophyll value by algorithm and actual chlorophyll

value measured by chlorophyll meter is shown in Figure 5.20. The chlorophyll meter

readings has already been found to be well correlated with leaf chlorophyll content

extracted through organic solvent method. The Root Mean Square Error (RMSE) of

estimated values of chlorophyll by algorithm with respect to chlorophyll measured by

chlorophyll meter is 1.9334, which clearly indicates that the values are almost overlap-

ping each other. Tabular representation of measurement of chlorophyll by algorithm

and by chlorophyll meter of these 120 samples is given in Appendix-D.

5.4 Disease Severity Measurement

Plant disease symptoms can be measured in various ways [82] that quantify the inten-

sity, prevalence, incidence and severity of disease.

1. Disease intensity is a general term used to describe the amount of disease present

in population.

2. Disease prevalence is the proportion of fields, countries, states etc. where the

disease is detected and reveals disease at grandeur scale than incidence.

77

3. Disease incidence is the proportion or percentage of plants diseased out of a total

number assessed.

4. Disease severity is the area (relative or absolute) of the sampling unit (leaf or

fruit) showing symptoms of disease. It is most often expressed as a percentage

or proportion.


In this work disease severity measure has been considered particularly for brown spot

diseased sugarcane leaves.

Brown spot is a fungal disease which causes reddish-brown to dark-brown spots

on sugarcane leaves. The spots are oval and circular in shape, often surrounded by

a yellow halve and are equally visible on both sides of the leaf [31]. The long axis of

the spot is usually parallel to the midrib. They can vary from minute dots to spots

attaining 1 cm in diameter.

The disease severity of the plant leaves is measured by counting the number of

pixels and defined as the ratio of number of pixels in diseased region and total pixels

in leaf region [83]. It is expressed as below:

Disease Severity, S =Ad

Al

(5.22)

Where,

Ad = Diseased leaf area,

Al = Total leaf area.

Here,

Ad = Pd =∑

(x,y)∈Rd

L(x, y) (5.23)

Al = Pl =∑

(x,y)∈Rl

L(x, y) (5.24)

S =Pd

Pl

(5.25)

Where,

L = Leaf image,

Pd = Total pixel in diseased region,

Pl = Total pixel in leaf region,

78

Rd = Diseased region and

Rl = Leaf region.

In sugarcane agronomy it is experimental that the length and width of leaf vary

upto 1.5 meter and 3 inches respectively. To cover the whole leaf area in the image,

the distance between camera and leaf must be adjusted proportionally. This tends to

reduce the resolution of image and cause poor segmentation of diseased part of the

leaf. Consequently to increase the accuracy of measurement of disease severity, the

leaf is cut into the pieces. Another important subject in image capturing is that the

evaporation rate of sugarcane is 150 to 200 times faster than other plants. Therefore

sugarcane leaf tends to wrinkle directly after it is separated from the stalk. So, it is

advised to capture the images quickly (without delay).

Today there are no standards to measure the extent of disease severity, [31] there-

fore experiment has been carried out to test the performance of the proposed system.

This experiment consists of drawing of standard shapes with a predefined dimension

drawn using a tool such as Paint available as accessories of Microsoft Windows Oper-

ating System is shown in Figure 5.21. The objective of this experiment is to check the

accuracy of the algorithm of measurement of disease severity. Therefore, the known

shapes such as Circle, Triangle, Square and Rectangles are given as an input to the

algorithm.

Imagination is that a green rectangle as a leaf area and small other known areas are

the disease areas. During the experimentation, these small areas and their combinations

are given as inputs to algorithm and compare the output of the algorithm as measured

area with predefined area.

Figure 5.21: Known area standard diagram

79

Physical descriptors are computed as: Percentage Accuracy (AC) and Percentage

Deviation Factor (DF) is calculated from the equation 5.9 and 5.10 as:

DF = (SM − EM)×100

SMand AC = 100−DF

The total area of green part corresponding to leaf is 61.05 cm2 and area of circle,

rectangle, square, triangle and their addition of all areas are 6.81, 0.9, 1.98, 2.49 and

12.19 cm2 respectively is shown in Figure 5.21.

To compute the disease severity more accurately the proposed system work into four

different steps that is: capture the image, compute the diseased leaf area, compute the

total leaf area and calculate the disease severity. The detailed discussion on all these

four steps is given in next subsequent subsections.


To calculate the brown spot disease severity of sugarcane leaf the designed algorithm

is as follows:

Algorithm :

1. Acquire the image of diseased leaf

2. Preprocessing of image to convert in proper format:

• Resize the image

• RGB to Grayscale conversion

• RGB to HSI conversion

3. Segmentation of leaf region

4. Computation of leaf area

5. Segmentation of diseased region

• Morphological processing

6. Computation of diseased area

7. Computation of the percentage diseased severity using diseased area and leaf

area.

80

Figure 5.22: Original image of diseased leaf

1. Acquire the image of diseased leaf : The following describes the experimental

set-up that was used for image acquisition.

(a) The leaf was placed on a light panel so that the spots become more visible.

(b) Light sources of cool white fluorescent light were placed at 450 on each side

of the leaf so as to eliminate any reflections and to get even light everywhere.

(c) The camera was placed just above the leaf so as to get a better snapshot.

(d) To distinguish objects in an image an easily separable background is essential

and hence white background was used in this experimentation.

The sample image of diseased leaf is shown in Figure 5.22. This image is used

for further processing.

2. Preprocessing of image : These steps convert input sample image in more

suitable format for the segmentation.

• Re-size the image : A digital camera used for capturing image was 12

mega pixels, input colour image size is 4000 × 3000, this image is down

sampled to 256 × 256 for low processing time. The down sampling of an

image is performed by the process of the interpolation [44]. The resized

colour image is used for gray scale conversion.

• RGB to Grayscale conversion : To reduce the complexity of obtaining

leaf region and region of interest, background should be removed first there-

fore input image is converted to the grayscale intensity image. The input

81

Figure 5.23: Gray scale image of diseased leaf

colour image is converted to gray scale using equation (5.2) is as below:

I = 0.3R + 0.59G+ 0.11B

For proper contrast, the image of diseased leaf was taken on white back-

ground results into large difference in gray values of two region i.e. leaf

region and background. This gray scale image is, shown in Figure 5.23,

further used for separating leaf region from the background.

• RGB to HSI conversion : Certain aspects influencing the experimental

process are:

(a) The image quality is influenced by light changes and shadows.

(b) The vein colour is usually shallower than the leaf colour and in the early

stages of disease the green colour often fades. Therefore in the course of

disease segmentation; the phenomenon of the wrong segmentation often

exists [84, 85].

RGB colour space would not be suitable for this problem. Considering

the above facts, the input RGB colour space is transformed to HSI colour

space. HSI space is more suitable for visual characteristics of human beings.

Since the brightness component is independent of the colour component,

the colour component can be good to eliminate glare, shadow and other

light factors during colour image segmentation [80]. The sample leaf image

converted into HSI and H component of sample image is shown in Figure

5.24. The H component image is used for diseased region segmentation.

82

Figure 5.24: RGB to H space converted image of diseased leaf

3. Image segmentation : In many vision applications, it is useful to isolate the

regions of interest from the background. The level to which the subdivision is

carried out depends on the problem being solved. That is, segmentation should

stop when the objects of interest or the regions of interest in an application have

been isolated. Thresholding techniques for segmentation were used [42].

Thresholding often provides an easy and convenient way to perform segmenta-

tion on the basis of the different intensities or colours in the foreground and

background regions of an image.

In this section two different thresholding techniques are implemented to obtain

total leaf region pixels and pixels of diseased region of leaf. Brief understandings

of these techniques are as:

• Leaf region segmentation : To count the total pixels in leaf region they

must be separated from the background. The reflectance of the sugarcane

leaf is stronger than its background in grayscale images. Therefore the

following threshold function is employed to segment the leaf from the back-

ground.

83

gi(x, y) =

0 fi(x, y) ≤ Ti

fi(x, y) fi(x, y) ≥ Ti

(5.26)

Where, gi(x, y) is the segmented gray level at pixel (x, y)

fi(x, y) is the original gray level at pixel (x, y)

Ti is threshold value and i represents the red, green and blue components.

The use of histogram to separate the region is a common practice. Histogram

of gray scale image is as shown in Figure 5.25. It can be seen that it

has bimodal characteristics. The left wave crest corresponds to the leaf

region with lower gray value. There are splits between the background

region and its corresponding wave crest. It can also be seen that there is

a large difference between the gray value of leaf and background (distance

between wave crest is more than 100 in case of sample image). Thus simple

thresholding technique (using a single threshold value) would not give a

good result [83].

Figure 5.25: Histogram of grayscale image of diseased leaf

Otsu thresholding technique is used to separate the leaf region form the

background [52, 53]. Otsu technique is an overall automatic threshold se-

84

Figure 5.26: Binary image of diseased leaf

lection method, which calculates co- variance between the two groups i.e.

between leaf region and background. Largest value of co- variance is selected

as threshold value. For the sample image threshold value comes out to be

0.4902 and resultant binary image for the same is shown in Figure 5.26. The

total pixels corresponding to the leaf region can be easily counted from this

binary image.

4. Computation of leaf area : To count the total white pixels in leaf region ,

the binary image is scanned in top-down format. The number of white pixels in

the white non-zero region is counted using equation (5.14), Pl =∑

(x,y)∈Rl

L(x, y) .

The leaf pixels count for the sample image comes out to be 30913.

5. Disease region segmentation : The accurate segmentation of disease region

determines the success or failure of the experiment. As the diseases characteris-

tics varies the boundaries between diseased and healthy part of leaf also varies

which results in a weak edge.

Another problem is the presence of boundary objects. The boundary objects are

not complete and thus may hinder the process of accurate segmentation therefore

the boundary objects have to be removed. Hence triangle thresholding method

is used in this experiment [86].

85

Figure 5.27: Triangle method to select threshold value

Histogram of sample image is as shown in Figure 5.27. To select the thresholding

value, triangle is constructed by drawing a line between the maximum peak of

the histogram at brightness bmax and the lowest value bmin in the image. The

distance ‘d′ between the line and the histogram h(b) is computed for all values

of ‘b′ from ‘b′min to ‘b′max. The maximum value of distance ‘d′ is selected as the

threshold value.

In sample image, the coordinates of point ‘b′min are (13, 2), ‘b′max (175, 3220)

and the threshold value is 0.0939. Using the resultant threshold, ‘H’ component

of the HSI image is thresholded to convert the Hue image into binary image as

shown in Figure 5.28.

Further, this binary image is morphologically processed to fill holes in the binary.

A hole is a set of background pixels that cannot be reached by filling in the

background from the edge of the image. Thus the resultant image is one that

would not have noise pixels at boundary, as shown in Figure 5.29.

6. Computation of diseased area : For the sample image shown in Figure 5.19

the pixel count of diseased region image (Rd) is given by equation (5.15), Pd =∑

(x,y)∈Rd

L(x, y) and is come out to be 2730.

86

Figure 5.28: Binary image of diseased leaf

7. Computation of the percentage diseased severity using diseased area

and leaf area : Disease severity is calculated by using equation (5.20) and

always expressed in percentage as:

S =

∑

(x,y)∈Rd

L(x, y)

∑

(x,y)∈Rl

L(x, y)

%S =2730

30913× 100

%S = 8.8312

The measured disease severity is expressed in a more quantitative way by using

disease severity scale developed by agricultural scientist ‘Horsfall and Heuberger’

Figure 5.29: Filtered image of diseased leaf

87

[82]. They developed early interval scale, which is still widely perceived as a way

to save time in disease assessment. A score of disease in plants is calculated and

rated on the scale. Thus according to Table 5.2 disease severity of sample image

is of category 1.

Table 5.2: Disease severity scale developed by Horsfall and Heuberger

Category Severity

0 Not infected

1 1− 25% leaf area infected



4 >75% leaf area infected


In agriculture research laboratory graphical method is used to measure the disease

severity of those leaves whose veins structure is individual to carry out water and nu-

trients to and from the leaves (e.g. Soybean, Betel). In these cases it is easy to cut the

diseased part of the leaf and to mark on the graph paper.

In sugarcane leaf veins are found in bundles that is called fibro vascular bundles

that carries water and nutrients to and from the leaves. In addition to it there can

be found other fibrous tissue for aiding in shaping and mechanically strengthening the

leaves. This makes difficulty to cut the sugarcane leaf at diseased place to mark on

the graph paper. Another issue related to graphical method not suitable for sugarcane

leaf disease severity measurement is that the evaporation rate of sugarcane is 150 to

200 times faster than other plants. Therefore sugarcane leaf tends to wrinkle directly

after it is separated from the stalk.

Traditionally the naked eye observation method is used in the production practises

to measure the severity of disease. However the results are subjective and are not

accurate.

For validation of proposed algorithm standard area diagram is designed as discussed

earlier in this chapter.

88

This section describes the detailed test strategies and the results obtained. Test

cases and experiments have been built to test accuracy of the algorithm. A total of 80

diseased leaves are tested using this algorithm. A tabular representation measurement

of disease severity of these 80 samples is given in Appendix E.

As per the algorithm developed in this work, area measurements for the pre-defined

shapes are taken. A comparison is made between the two sets of values and through

this the accuracy of the software is measured.

Physical descriptors are computed by referring equation (5.9 and 5.10) as:

DF = (SM − EM)×100

SMand AC = 100−DF

Accuracy and results for individual feature is listed in Table 5.3.

Table 5.3: Comparison of measurement of features

Sr.

NoShape

Standard

Measure

(SM)

Expt.

Measure

(EM)

Deviation

(DF)

Accuracy

(AC)

1 Circle 6.81 6.6 3.1 97

2 Rectangle 0.9 0.9 0 100

3 Square 1.98 1.99 1 99

4 Triangle 2.49 2.45 1.61 98

5 Group of all above 12.19 12.42 1.87 99

6 Circle and Rectangle 7.71 7.5 2.72 97

7 Circle and Square 8.79 8.59 2.27 98

8 Circle and Triangle 9.3 9.05 2.69 97

9 Rectangle and Square 2.88 2.89 1 99

10 Rectangle and Triangle 3.39 3.35 1.18 99

11 Square and Triangle 4.47 4.44 3 97

The average accuracy of the entire feature comes out to be 98.60%, where in the

rectangle feature gives ideal results. The above data confirms the accuracy (validity)

of the system for measurement of the disease severity.

5.5 Concluding Remarks

Algorithm developed for growth measurement of sugarcane plant tested on different

conditions of the +1 dewlap. The results of designed algorithm are compared with

results of traditional method used to measure the growth of sugarcane plant (i.e.

89

protector- meter scale method) for validation.

The accuracy of measurement of the growth by algorithm is greater than 96% and

RMSE value 0.2872, indicate that there is a good agreement between growth measured

by using meter scale and proposed method. The accuracy is higher at constant light

condition, detecting the boundary curvature. We could find the axils, the leaf tips and

the dewlap. However it was difficult to determine the dewlap of the +1 leaf if the 0

leaf was almost fully developed.

In determination of chlorophyll content of sugarcane leaves using HSV colour space.

Linear mathematical HSV model is proposed to co-relate with the chlorophyll content

apart from the simple correlation analysis. For the validation of results of proposed

algorithm these results are compared with results of chlorophyll meter. The resultant

RMSE value is 1.9334, indicates that there is a good agreement between chlorophyll

measured by chlorophyll meter and proposed method.

Disease symptoms of the plant vary significantly under the different stages of the

disease. Hence the accuracy depends to some extent upon segmentation of the image.

Otsu threshold segmentation is used for leaf region segmentation but this thresholding

method is not suitable to disease region segmentation because of varying characteristics

of the disease. Triangle method of the thresholding is used to segment the disease

region. There is no any standard method to measure the disease severity of sugarcane

leaf so for the validation of the results of designed algorithm known area standard

diagram is designed. The average accuracy of the algorithm is tested using known

area standard diagram and is 98.60%. This indicates that the designed algorithm can

measure the disease severity of the leaf more accurately.

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chapter 5 image processing method for crop status...

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