chlorophyll content estimation using image ...

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Chlorophyll Content Estimation Using Image Processing Technique Mahmood Mahmoodi, Javad Khazaei, Kourosh Vahdati, Narjes Mohamadi and Zeinab Javanmardi Department of Agricultural Technical Engineering, College of Abooraihan, University of Tehran, Tehran, Iran, P.O. Box: 11365/4117 Department of Horticultural Sciences, College of Abooraihan, University of Tehran, Tehran, Iran Department of Agronomy and Crops Breeding, College of Abooraihan, University of Tehran, Tehran, Iran Abstract: One of the major components of plant cell is chlorophyll and its content plays an important role in plant functions. In this research the chlorophyll (Chl hereafter) content of leaves of five walnut varieties was measured using chemical and SPAD methods. Then the Chl content values were compared with colour features of leaves to determine the correlation between Chl content and leaf colour features. To determine the colour features, image processing technique was used and the colour components of red (R), green (G) and blue (B) in RGB space and hue (H), saturation (S) and intensity (I) in HSI space were determined. In compression of the Chl content with colour features in RGB space, there were not strong correlations between Chl content with red, green and blue components. Therefore, analysis was carried out using combined data such as, (R-B)/(R+B) function. The measured Chl contents were also compared with hue, saturation and intensity components and the correlations were more highlighted. The analyses showed that there were correlation coefficients of -0.78 and 0.87 between (R-B)/(R+B) and hue with Chl content, respectively and hue colour component had the most correlation coefficient with Chl content among the analyses. Key words: Chemical method % Chlorophyll content % Image processing % Modeling % Walnut Nomenclature: R G B WL

Red Green Blue Walnut leaves

H S I

Hue Saturation Intensity

INTRODUCTION

WV Chl LW

Walnut variety Chlorophyll Leaf weight

the Chl content of plant tissues to be determined for such studies on photosynthesis [1]. The Chlorophyll, both Chla and Chlb, are virtually essential pigments for the conversion of light energy to store chemical energy. The amount of absorbed solar radiation by a leaf largely depends on the photosynthetic pigment content; therefore, Chl content can indirectly determine the photosynthetic potential and primary production [2-4]. Regarding the fact that leaf colour can indicate amount of Chl in leaves and the Chl content in the leaves is closely

During the last half century, plant biochemist studied the effects of different light regimes, nutrients and other growth conditions on the efficiency of various photosynthetic indexes including O2 evolution, CO2 fixation, or carbohydrate biosynthesis. Because of fundamental role of chlorophyll (Chl) in photosynthesis, the rate of these indexes was often presented per unit of Chl expressed in mass or molar terms; make it inevitable

Corresponding Author: Mahmood Mahmoodi, Department of Agricultural Technical Engineering, College of Abooraihan, University of Tehran, Tehran, Iran, P.O. Box: 11365/4117. Tel: +989128314857, Fax: +982913025200, E-mail: [email protected].

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related to plant nutrient status [5], as representative of Chl content of plant, it can merely be used as an index to estimate nutrient status. Various functions of colour components (for comparisons) and Chl meters (which give an arbitrary value) have been developed to estimate Chl content [3, 6, 7]. Haboudane et al., [6] found correlations between Chl content and canopy reflectance particularly in the spectral region from green (550 nm) to red edge (750 nm) whereas the same correlation was found by Kim et al. in the different spectral region ranging in visible light [8]. The image processing techniques have recently been used for remote sensing studies concerning agrarian monitoring projects. It is precisely because of by far easier acquisition of images and the facility of being available as real-time database that image processing has been captured the attention of researchers as alternative strategy. In this point of view, image processing has been intensively used from the outset of introduction. Spomer et al. described a practical, micro-computerized image analysis method for quantifying leaf Chl content from a monochrome video image [9]. Everitt et al. presented the status and development of such airborne video imaging systems for resource management, with special emphasis on their application in agriculture [10]. Anatoly et al. found some relationships between leaf Chl content and spectral reflectance and algorithms for nondestructive Chl assessment in higher plant leaves [11]. Gao estimated canopy’s Chl with hyper-spectral remote sensing [12]. As it was reaserched by Kawashima and Nakati [13] and Mahmoodi et al. [14], the colour components of RGB space were nearly related to Chl content of leaves, It has remained to be seen whether colour components of HSI space are appropriate index for determining Chl content of walnut leaves (WL) [13, 8]. Hue, Saturation and Intensity (H-S-I) space is established according to human colour perception which is normally limited to visible light. The H and I components are generally related to the wavelength and the amplitude of a light, respectively. The last but not the least, S is a component which measures the “Colourfulness” in the HSI space [15]. In this research, some algorithms have been developed to determine the correlation between Chl content and colour features of the WL and sought whether colour components of HSI space are appropriate index for determining Chl content of WL. We also compared chemical method with SPAD method to determine the SPAD method accuracy.

MATERIALS AND METHODS Plant Materials: The leaf samples were taken in 1 October to 5 December 2008 from a number of walnut cultivars at Seed and Plant Improvement Institute (SPII), Karaj, Iran. Four commercial cultivars, ‘Lara’, ‘Franquette’, ‘Hartley’ and ‘Pedro’ and a promising native variety ‘K72’ were sampled (Figure 1). The sampled leaves were under stress that affects Chl content. From each cultivar, the 30 leaf samples were chosen. The experiments were performed in three steps. First of all, as a non-destructive test, the leaves were studied in three different points of each sample using SPAD method in the field. Then the images were taken in the laboratory and finally the Chl extracted from the leaves using chemical method. SPAD Method: Chlorophyll content per unit area of each leaf was measured in the field with a Chl meter (SPAD-502; Minolta, Tokyo, Japan). The SPAD-502 measures the Chl content in the field as a fast, compact and easy. It had a measuring limitation of 2×3 mm with the accuracy of ±1.0 SPAD unit. The measurement principle for the Chl meter was transmittance at 650 nm (red) and 940 nm (near infrared) wavelengths used in ‘equation (1)’: SPAD = K log[

IR1 / IR0 ] R1 / R0

(1)

SPAD : Chl content (SPAD) value K : Constant IR1 and R1 : Transmittance of NIR (940 nm) and red (650 nm), respectively IR0 and R0 : Incident power of NIR and red, respectively SPAD values are linearly related to leaf Chl content. The weak point is that the constant coefficient in the equation (K) should be corrected for plant species and individual SPAD meter [16]. Therefore, to determine the Chl content with the SPAD-502 meter, a calibration against Chl content determined with chemical analysis was required. Image Processing Method Image Acquisition: The leaves which were surgically removed from the plants were placed flat on the light box and photographed (Figure 2). The orientation placement of the leaves in the light box was random. A digital colour camera (Model G7 Canon, Japan) with a resolution of 480 × 640 pixels was used to record images. The camera located vertically over the light box at a distance of 30 cm.

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Fig. 1: Pictures taken from leaves of walnut varieties including ‘Lara’ (a), ‘Franquette’ (b), ‘Hartley’ (c), ‘Pedro’ (d), ‘K72’ (e) by (the camera).

Fig. 2: Schematic of light box with adjustable light and changeable distance. The images were taken on a black colour textile which can easily be subtracted by standard segmentation routines due to the colour contrast between background and samples.

Image Segmentation: Image segmentation - subdividing an image into different parts or objects - was used as the first step in image analysis. The images were subdivided until the interested objects can be 3

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distinguished from their background. In segmentation algorithms, the thresholding, region growing, region splitting and then merging were assayed. Act of thresholding was an important part in image segmentation. The threshold value was presented as constant value for the same environmental conditions. In this research the threshold was calculated using a mask which was determined by using the covariance matrix of a part of the leaf images. In this work, the colour components of red (R), green (G) and blue (B) in RGB space were determined from images of each leaf. Then during the colour analysis, the HSI space calculated using the RGB space [17]. Colour spaces- RGB and HSI - can be transformed from one to another easily as illustrated in ‘equations (2) - (4)’. 1 I = ( R + G + B) 3 S =1−

3 [min( R, G, B)] ( R + G + B)

[( R − G ) + ( R − B )]/ 2 H = arccos{ } [( R − G ) 2 + ( R − B )(G − B )]1/ 2

Chla = (13.7 × A663.6) – (2.85 × A646.6)

(5)

Chlb = (22.39 × A646.6) – (5.42 × A663.6)

(6)

Chl(a + b) = (8.29 × A663.6) + (19.54 × A646.6)

(7)

RESULTS SPAD Method: The mean of Chl content measured using SPAD method recorded as 36.82, 29.40, 38.27, 41.22 and 13.96 SPAD for ‘Lara’, ‘Pedro’, ‘Hartley’, ‘Franquette’ and ‘K72’, respectively (Figure 3). The minimum Chl content recorded for ‘K72’ and it ranged between 5.4 and 22.1 SPAD. That of ‘Franquette’ was maximum and it varied between 25.7 and 49 SPAD. Chemical Method: The total Chl contents determined by chemical analysis were recorded as 0.24, 0.21, 0.23, 0.27 and 0.12 [(mg Chl)/(gr LW)] for ‘Lara’, ‘Pedro’, ‘Hartley’, ‘Franquette’ and ‘K72’, respectively (Table 1). When considering different Chls separately, the Chla ranged from 0.12 to 0.15, 0.11 to 0.13, 0.13 to 0.16, 0.12 to 0.18 and 0.055 to 0.08 [(mg Chl)/(gr LW)] for ‘Lara’, ‘Pedro’, ‘Hartley’, ‘Franquette’ and ‘K72’, respectively. Corresponding values of Chlb varied from 0.09 to 0.013, 0.07 to 0.11, 0.06 to 0.14, 0.09 to 0.14 and 0.045 to 0.06 [(mg Chl)/(gr LW)]. The total Chl content of ‘K72’ was the lowest among the varieties and ranged from 0.10 to 0.14 [(mg Chl)/ (gr LW)]. On the other hand, the total Chl content of ‘Franquette’ ranged from 0.24 to 0.33 [(mg Chl)/(gr LW)] and showed the highest value of Chl content among the varieties.

(2)

(3)

(4)

Feature Extraction: The algorithms were developed to determine the colour features by using MATLAB 7.2 software. Finally, methods for determining the correlations between Chl content and leaf colour features were examined. Chemical Method: After examination of the samples by the two nondestructive methods, SPAD methods and image processing, chemical analysis of samples deemed as a destructive method was adopted to measure the Chl content of leaves in the aforementioned walnut varieties (Wvs) according to Arnon equations [18]. Extraction was executed from 9 cm2 of each leaf which was cut with scissor and homogenized in 80% acetone. After centrifuging, the supernatants were collected in a test tube and then the residue was extracted again with 80% acetone to ensure complete extraction. The final volume of each test tube reached to 25 ml by adding 80% acetone. The absorbance of extract at 663.6 and 646.6 nm was recorded in a 300 microliter cell of spectrophotometer. The concentration of Chla, Chlb and Chl(a+b) were determined by using empirical ‘equations (5) - (7)’ [18].

Fig. 3: Chl content of five walnut varieties, ‘Lara’, ‘Pedro’, ‘Hartley’, ‘Franquette’, ‘K72’, using SPAD method. 4

Chlorophyll Content of Leaves (mg Chl/gr LW)

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When methods to determine the correlation between the Chl content and R, G and B components of leaf images were compared, there were not significant correlations between these components and Chl content among varieties (Table 3). Therefore, analysis was performed on combined data of different values of colour components. Various functions were derived from the R, G and B values (presented in the second column of Table 3). The function (R-G) comprises, for instance, the difference between the R and G components and (R-G)/(R+G) represents the difference normalized by the sum of the components. The values of R-B and G-B were the corrected values of R and G by using B as a base value. As this operation reduced the bias noise of R and G, R-B and G-B were more correlated with Chl than that of R and G. Correlation coefficients between the values of these functions and Chl content of each variety are also calculated (Table 3). In RGB space, R-B showed the highest correlation coefficients changing from -0.33 to 0.68 and showed the most appropriate outcome on some variety, whereas did not perform as well when data collected for all varieties were analyzed together. The relation between R-B against Chl content of overall varieties is overlaid as ‘figure 5’. The correlation coefficient between R-B and Chl content was -0.75. Gain noise appeared as a linear shift regarding Chl content. The camera could not reduce the gain noise uniformly; however for the data obtained under the natural conditions, it is necessary to reduce the noise in order to generalize the estimation. Normalizing is one method to reduce this type of noise and normalized difference relations, (R-B)/(R+B) and (R-G)/(R+G),

0.3 y = 0.0054x + 0.046 R2 = 0.9957

0.25 0.2 0.15 0.1 0.05 0 0

10

20

30

40

50

Chlorophyll Content of Leaves (SPAD)

Fig. 4: Correlation between Chl content estimation using chemical and SPAD methods for five walnut varieties. For calibrating the Chl meter (SPAD), the measured Chl content with chemical method was used. The regression between data obtained from chemical analyses and SPAD values of total WVs showed a linear relationship with (R2 = 0.996) and correlation value was upper than 0.99 (Figure 4). It was illustrated that the Chl content of ‘K72’ was the lowest and ‘Franquette’ was the highest among the varieties. Image Processing: The mean values of colour components (R-G-B) in the form of digital numbers ranged from 0 to 256. The colour component distributions of each variety were considered and it was concluded that the median of B component was considerably deviated from those of R and G components for the WVs. The mean value of B component varied from 26.02 for ‘Hartley’ to 33.25 for ‘Pedro’ and it implicated nearly constant values between the varieties (Table 2).

Table 1: Statistical parameters of total Chl content (a+b) for different walnut varieties Statistical

Chl (a+b) (mg Chl/gr LW) --------------------------------------------------------------------------------------------------------------------------------------------------------------------

Parameters

‘Lara’

‘Pedro’

‘Hartley’

‘Franquette’

‘K72’

Mean

0.24

0.21

0.23

0.27

0.12

St.err

0.02

0.01

0.04

0.02

0.01

Min

0.18

0.19

0.15

0.24

0.10

Max

0.30

0.24

0.30

0.33

0.14

†chl= chlorophyll Table 2: Statistical Parameters of G and B colour components for different walnut varieties

Statistical Parameter Mean Std.err

‘Lara’

‘Pedro’

‘Hartley’

‘Franquette’

------------------

--------------------

-------------------

-------------------

‘K72’ ----------------

G

B

G

B

G

B

G

B

G

B

31.55

27.63

37.40

33.25

32.06

26.02

36.32

31.04

41.47

33.13

0.93

0.86

1.29

1.20

0.90

0.76

1.62

1.51

1.05

1.14

Min

20.59

18.48

23.28

19.88

21.59

18.49

22.83

20.05

28.60

21.06

Max

42.55

39.56

67.54

61.65

44.40

36.83

56.44

51.73

54.16

47.26

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WASJ Table 3: Correlation coefficients between various functions using R, G and B values and Chl content for different walnut varieties Chl content ---------------------------------------------------------------------------------------------------------------------------------------------------Channels

Various functions

RGB

R

HSI

‘Lara’

‘Pedro’

‘Hartley’

‘Franquette’

‘K72’

Overall

0.018

0.28

-0.05

0.23

-0.21

-0.30ns

**

G

-0.01

-0.70

B

0.07

R-G

0.37

R-B G-B

*

-0.22

0.29

0.29

0.15

0.22

0.25ns

0.33

-0.23

0.16

-0.23

-0.20ns

-0.68**

-0.63*

-0.53*

-0.33

-0.40

-0.75**

0.32

0.40

-0.27

0.24

-0.32

0.38ns

(R-G)/(R+G)

-0.19

-0.54*

-0.23

0.19

0.20

-0.51*

(R-B)/(R+B)

-0.66**

-0.59*

-0.65*

-0.44*

-0.34

-0.78**

*

*

0.39

0.87**

H

0.85

**

0.76

**

0.60

0.54

*

-0.41

-0.50*

-0.22

*

S

0.02

0.25

-0.17

-0.42

-0.41

-0.40ns

I

0.03

0.35

0.26

0.27

-0.30

0.21ns

*

† significant level at 5% †**significant level at 1% †ns non significant †chl= chlorophyll

Fig. 5: Correlation between R-B and Chl content for all walnut varieties.

Fig. 6: Correlation between (R-B)/(R+B) and Chl content for all walnut varieties. are applicable functions which can use of data collected under different natural conditions. The mean value of (R-B)/(R+B) varied from 0.036 for ‘Lara’ to 0.105 for ‘K72’ and that of (R-G)/(R+G) ranged from -0.039 for ‘Hartley’

to 0.010 for ‘Pedro’. The lowest value of (R-B)/(R+B) was recorded for ‘Lara’ and the determined value of function in ‘K72’ was more than the other varieties (Figure 6). The normalized (R-B)/(R+B) was found to be

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Satistical Parameter

‘Lara’

‘Pedro’

‘Hartley’

‘Franquette’

-------------------

--------------------

--------------------

------------------

--------------------

H

H

H

H

H

S

S

S

S

‘K72’ S

Mean

1.58

0.07

1.10

0.08

1.52

0.11

1.60

0.09

0.95

0.14

Std.err

0.04

0.01

0.01

0.01

0.02

0.01

0.03

0.01

0.02

0.01

Min

1.17

0.02

0.88

0.03

1.23

0.03

1.07

0.04

0.87

0.04

Max

1.88

0.11

1.19

0.12

1.85

0.20

1.89

0.16

1.21

0.28

Fig. 7: Correlation between H colour component and Chl content for all walnut varieties. negatively correlated (ranged from -0.34 to -0.66) with Chl contents of leaves among the varieties. It was found that (R-B)/(R+B) was the fitted function of RGB space to estimate the Chl content of leaves using only the data from the digital camera. Using data overall the varieties, correlation coefficient between Chl content and (R-B)/(R+B) showed the highest correlation coefficient in RGB space which was -0.78 (Figure 6). The results in HSI space showed that the mean of I colour component calculated for five varieties ranged from 29.01 for ‘Hartley’ to 38.39 for ‘K72’. Furthermore the mean values of S colour component varied from 0.07 for ‘Lara’ to 0.14 for ‘K72’ (Table 4). The value of H colour component of ‘Franquette’ recorded as highest whereas it was the lowest value in ‘K72’ (Figure 7). The maximum and minimum values of H component of ‘Franquette’ were 1.89 and 1.07, respectively (Table 4). Corresponding values of ‘K72’ were 1.21 and 0.87, respectively. The H component was found to be positively correlated (ranged from 0.39 to 0.85) with the Chl contents of leaves among the varieties (Figure 7). Using data overall the varieties, correlation coefficient between the H values and Chl content was 0.87 and showed the highest correlation coefficient among the colour features. The regression between H values and Chl content of total WVs showed a linear relationship with (R2 = 0.76). The slope of the regression

line and the intercept were 6.27±0.03 (St.err= 2.5%) and 12.56±0.04 (St.err = 3%), respectively and the root mean square of residuals was 4 SPAD. Exploiting might be limited in vivo due to inherent variation of the intensity in the outdoor lighting conditions. The image colour feature dataset, which applies a reduced H feature set, emerged as the best model for Chl content estimation of WL in the present study. It also eliminated the intensity features which may be beneficial in such a highly variable outdoor lighting conditions. Furthermore the analysis proved that such methods can be literally used for estimating Chl content of WL while samples examined under controlled laboratory lighting condition. DISCUSSION The present study showed that the properties of leaf colour in the RGB space were sensitive to Chl content which is in agreement with those of obtained by Kawashima and Nakatani [13]. Their results also implicated that R-B shows the highest correlation with Chl content in some WVs. Furthermore, the highest correlation coefficient was obtained when (R-B)/(R+B) was tested. Our results confirmed aforementioned premise and showed that (R-B)/(R+B) is the most fitted function of RGB space to estimate the Chl content of leaves. 7

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Taking solar radiation into account, it was tried to improve the former method reported by Kawashima and Nakatani through combining the video camera data with those concerning simultaneous solar radiation [13]. Additionally, normalizing reduced the effect of solar radiation when the solar radiation was not examined at the same time. In this study, the H colour component also eliminated the intensity features, which may be beneficial in such a highly variable outdoor lighting conditions such as solar radiation and leaf angle. Conclusively, this model with low dependence on solar radiation provided the most appropriate estimation of Chl content and reduced the number of variable and consequently minimized computation duration.

6.

7.

8.

ACKNOWLEDGEMENT

9.

We would like to express our special thanks to the Dr Darab Hasani in SPII for supplying the samples in this research. Special thank is due to Mr. Ehsan Sari and Fateme Noroozi for his assistance in editing the manuscript.

10.

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