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Estimating rice chlorophyll content and leaf nitrogen concentration with a digitalstill color camera under natural light
Plant Methods 2014, 10:36

doi:10.1186/1746-4811-10-36

Yuan Wang (wangyuan@issas.ac.cn)
Dejian Wang (djwang@issas.ac.cn)
Peihua Shi (2009201012@njau.edu.cn)
Kenji Omasa (aomasa@mail.ecc.u-tokyo.ac.jp)

ISSN1746-4811

Article type

Methodology

Submission date

22 June 2014

Acceptance date

30 September 2014

Publication date

6 November 2014

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http://www.plantmethods.com/content/10/1/36

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Estimating rice chlorophyll content and leaf
nitrogen concentration with a digital still color
camera under natural light
Yuan Wang1
Email: wangyuan@issas.ac.cn
Dejian Wang1*
Corresponding author
Email: djwang@issas.ac.cn*

Peihua Shi2
Email: 2009201012@njau.edu.cn
Kenji Omasa3
Email: aomasa@mail.ecc.u-tokyo.ac.jp1

Institute of Soil Science, Chinese Academy of Sciences, 71 East Beijing Road,
Nanjing 210008, PR China
2

National Engineering and Technology Center for Information Agriculture,
Nanjing Agricultural University, 1 Weigang Road, Nanjing 210095, PR China
3

Graduate School of Agricultural and Life Sciences, The University of Tokyo,
Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan

Abstract
BackgroundThe color of crop leaves is closely correlated with nitrogen (N) status and can be quantified
easily with a digital still color camera and image processing software. The establishment of
the relationship between image color indices and N status under natural light is important for
crop monitoring and N diagnosis in the field. In our study, a digital still color camera wasused to take pictures of the canopies of 6 rice (Oryza sativa L.) cultivars with N treatments
ranging from 0 to 315 kg N ha−1 in the field under sunny and overcast conditions in 2010 and2011, respectively.

Results
Significant correlations were observed between SPAD readings, leaf N concentration (LNC)
and 13 image color indices calculated from digital camera images using three color models:
RGB, widely used additive color model; HSV, a cylindrical-coordinate similar to the humanperception of colors; and the L*a*b* system of the International Commission on Illumination.
Among these color indices, the index b*, which represents the visual perception of yellowblue chroma, has the closest linear relationship with SPAD reading and LNC. However, therelationships between LNC and color indices were affected by the developmental phase.
Linear regression models were used to predict LNC and SPAD from color indices and phasic

development. After that, the models were validated with independent data. Generally,
acceptable performance and prediction were found between the color index b*, SPAD readingand LNC with different cultivars and sampling dates under different natural light conditions.

Conclusions
Our study showed that digital color image analysis could be a simple method of assessing
rice N status under natural light conditions for different cultivars and different developmental
stages.

Keywords
Digital still color camera, Leaf color, Image processing technology, Natural light, Nitrogen,
Rice

Background
Nitrogen (N) is one of the most important nutrients essential for the growth of crops, and is amajor component of chlorophyll and protein which are closely associated with leaf color,
crop growth status and yield [1]. Insufficient N supply leads to smaller leaves, lower
chlorophyll content and less biomass production, and consequently, reduced grain yield andquality [2,3]. Conversely, excessive N application can lead to the environmental problems ofwater and atmospheric pollution [4]. Hence, measuring crop N status timely is critical forincreasing N use efficiency and environmental quality [5,6].
The spectral reflectance of crop leaf or canopy is known to be correlated with N status [7,8].
The instruments for measuring spectral reflectance are the chlorophyll meter [9-11], multispectral sensor [12], hyper-spectral sensor [7,8] and commercial digital camera [13-15], areused in precision agriculture for growth monitoring, nitrogen diagnosis and site-specific crop
management. The SPAD meter (SPAD-502, Minolta Camera Co., Osaka, Japan), measuresleaf chlorophyll content nondestructively, has potential for improving N use efficiency
without affecting grain yield in real-time nitrogen management (RTNM) experiments [9,11].
A disadvantage of the SPAD-502 for assessing crop N status is its small sampling area (6mm2). In addition, the measurements are subject to operator bias so a large number ofrepetitions are needed to obtain reliable results [16,17], and SPAD meter experiencesdifficulties in distinguishing chlorophyll levels when crops are near or above the optimum N
supply [10,18]. In contrast, satellite or airborne-mounted hyperspectral sensors can obtain
spectral information in a larger sampling area and record more spectral bands [7,8,12]. The
high cost of images, infrequent satellite overpasses and risk of images being obscured byclouds limit the application of these platforms for commercial use [19]. The proximal sensors
GreenSeeker® (NTech Industries Inc., USA) and Yara N-sensor (Yara International ASA,
Germany) which measure red and near infra-red (NIR) reflectance, overcome some of thelimitations of satellite or airborne sensors [20], but their accuracy is influenced bybackground soil interference [8].
Alternatively, images from digital still color cameras, which record spectral information ofvisible bands, have a low cost but very high image resolution (consumer cameras in 2014
record up to 40 megapixels per image). At a sensor height of about 1 meter above the canopy,
high-spatial-resolution images separate crops from background soil or other interferences,which is important for accurate diagnosis of N status when the vegetation fraction was low

[13,21]. Moreover, images from digital still cameras contain a large amount of information
about the crop structure and leaf color, such as leaf orientation, plant height, biomass
accumulation and leaf senescence [22-24], and these parameters are easy to obtain [13,25]
with existing software, such as MatLab® (MathWorks Inc.), the free-ware package ImageJ[26]. Previous studies showed that canopy cover estimated from the images was not only
highly correlated with leaf area index (LAI), aboveground biomass and N accumulation[13,27], but was also stable in varying environmental conditions [28,29]. In addition to
canopy cover, color digital images provide spectral information in the visible bands which areclosely related to the leaf N concentration (LNC) and SPAD readings [17]. Hunt et al. [30]found that the triangular greenness index (TGI), which was derived from red, green and blue
bands of a digital still color camera, was sensitive to leaf chlorophyll content of a whole
canopy.
The color-related indices from digital still cameras can diagnose crop N status [17,30,31].
Previous studies on the analysis of leaf color were mostly undertaken in controlled light
conditions [17,32-34]. This approach could reduce the impact of light on the image color, andeasily obtain a reliable relationship between N status and leaf color indices. However, theresults derived from the controlled light cannot be completely applied to natural light becauseof the variable light conditions [15,35,36]. Besides, there are still many uncertainties in theuse of digital still cameras for N diagnosis under natural light conditions, and further
validation is necessary to ensure the application in the field.
In this study, experiments with different N application rates were carried out in the fieldunder sunny and overcast conditions (1) to analyze the relationship between chlorophyll
content, LNC and canopy color related indices in different cultivars and stage of phasic
development, (2) to establish the possible models for the diagnosis of crop N status using
image color indices, and (3) to validate the applicability of the models under different naturallight conditions.

Results and discussion
Correlation of color related indices and crop nitrogen status
Correlations between two rice N parameters (leaf N concentration and SPAD reading) and 13
image-color related indices (Eq. 1–10) were calculated with individual and pooled cultivars
in 2011. Similar results were obtained from the three rice cultivars, i.e., Liangyoupeijiu,
Nanjing45 and Nanjing46. Therefore, only correlation coefficients from the Liangyoupeijiu
dataset (n = 72) and the pooled dataset (n = 240) were displayed in Table 1. SPAD readings
showed significant correlations with each color index except g in both datasets. Among thesecolor indices, L*, b*, R, G, B, r and INT were negatively correlated with SPAD readings,
while the other indices were positively correlated with SPAD readings. Most of the color
indices were closely correlated with LNC, however, the magnitude and direction of thecorrelation coefficients were not consistent with those between color indices and SPAD
readings. Overall, the indices derived from the CIE L*a*b* color model had relatively higher
correlation coefficients with SPAD readings and LNC. In this color model, the index b*
represents the visual perception of yellow-blue chroma which is similar with the leaf color
variation, and it has been used in many other studies for image color analysis [37,38].
Therefore, we select index b* as a representative for further analysis.

Table 1 Correlation coefficients between SPAD readings, leaf nitrogen concentration (LNC, g kg−1) and image color related indices(digital number from three color models: RGB, HSV and CIE L*a*b*)
SPAD
LNC
L*
a*
b*
b*/a*
H
R
G
B
r
g
b
INT
VIGreen

SPAD

LNC

1.00
0.32**−0.62**
0.61**−0.73**
0.60**
0.61**−0.63**−0.62**−0.37**−0.56**
0.05
0.27**−0.56**
0.67**

0.21
1.00
0.35**−0.04−0.35**
0.48**
0.40**
0.33**
0.35**
0.58**−0.54**−0.66**
0.69**
0.42**−0.10

L*−0.67**
0.48**
1.00−0.70**
0.54**−0.29**−0.39**
1.00**
1.00**
0.91**
0.19**−0.62**
0.27**
0.99**−0.85**

a*0.78**−0.26*−0.83**
1.00−0.72**
0.36**
0.42**−0.68**−0.71**−0.47**−0.35**
0.03
0.17**−0.64**
0.42**

b*−0.90**−0.06
0.73**−0.86**
1.00−0.91**−0.91**
0.56**
0.54**
0.15*
0.89**
0.31**−0.66**
0.45**−0.65**

b*/a*
0.76**
0.35**−0.43**
0.50**−0.86**
1.00
0.97**−0.34**−0.29**
0.09−0.99**−0.44**
0.79**−0.20**
0.62**

H

R

G

B

r

g

b

INT

VIGreen

0.83**
0.13−0.63**
0.69**−0.95**
0.96**
1.00−0.44**−0.39**−0.03−0.95**−0.34**
0.71**−0.31**
0.68**−0.68**
0.47**
1.00**−0.82**
0.74**−0.45**−0.64**
1.00
1.00**
0.90**
0.24**−0.60**
0.23**
0.99**−0.88**−0.67**
0.48**
1.00**−0.84**
0.73**−0.43**−0.63**
1.00**
1.00
0.91**
0.19**−0.61**
0.26**
0.99**−0.85**−0.48**
0.63**
0.96**−0.69**
0.51**−0.18−0.40**
0.96**
0.96**
1.00−0.20**−0.87**
0.63**
0.95**−0.68**−0.76**−0.37**
0.38**−0.52**
0.87**−0.99**−0.95**
0.39**
0.38**
0.12
1.00
0.56**−0.87**
0.10−0.51**

0.01−0.68**−0.64**
0.18
0.04−0.26*−0.09−0.64**−0.64**−0.82**
0.36**
1.00−0.90**−0.70**
0.43**

0.41**
0.65**
0.21
0.17−0.51**
0.72**
0.58**
0.21
0.21
0.48**−0.79**−0.86**
1.00
0.37**
0.02−0.63**
0.52**
1.00**−0.80**
0.68**−0.37**−0.58**
1.00**
1.00**
0.98**
0.31**−0.69**
0.29*
1.00−0.83**

0.66**−0.28*−0.90**
0.62**−0.73**
0.63**
0.75**−0.92**−0.90**−0.83**−0.56**
0.58**−0.08−0.89**
1.00

Numbers in the upper diagonal were calculated with the Liangyoupeijiu (n = 72) dataset and numbers in the lower diagonal were calculated with the combined data of Liangyoupeijiu,
Nanjing45 and Nanjing46 in 2011 (n = 240).
**
indicate the significance at P < 0.01, * indicate the significance at P < 0.05.

Relationships between SPAD, LNC and the color index b*
Regression analyses were performed between SPAD readings, LNC and the color index b*using the 2011 dataset. Positive linear relationships were observed between LNC and SPAD
readings, with the same trends in different sampling dates and cultivars (Figure 1). The
determination coefficient (R2) in different sampling dates varied from 0.61 to 0.88 along withthe root mean square error (RMSE) from 1.81 to 2.64 g kg−1. The LNC decreased with therice development, while the maximum SPAD values increased with rice growth. Smaller
RMSE was obtained in jointing and booting stages than in vegetative and tillering stages.
Similarly, Xue et al. [39] reported that the ratio index of NIR/green (R810/R560) reached thebest accuracy with LNC at jointing stage. When data were pooled across the sampling dates,
there was no significant trend observed between SPAD readings and LNC (Figure 1e).
Figure 1 Relationships between leaf nitrogen concentration (LNC, g kg−1) and SPAD
readings in vegetative (a), tillering (b), jointing (c), booting (d) stages, and the pooleddata of the four stages (e) in 2011.
Significant negative linear relationships were seen between LNC and color index b*, with R2
ranging from 0.58 to 0.86 in the four developmental stages (Figure 2). The R2 between b* andLNC at booting stage (Figure 2d) was less than that between SPAD readings and LNC. Sameas the relationship between SPAD readings and LNC, no significant trend was observed
between b* and LNC when data pooled across the sampling dates (Figure 2e). In addition,
there were negative linear relationships between b* and SPAD readings (Figure 3). The R2 inthe vegetative stage was lower than that in the other stages. There were obvious differencesamong cultivars for the relationship between b* and SPAD readings, especially the
Liangyoupeijiu in jointing and booting stages (Figure 3c and d) and the Nanjing45 invegetative stage (Figure 3a). In this case, the regression analysis was carried out withindividual cultivars (Table 2). Overall, higher R2 were observed from individual cultivars andsampling dates than that from the pooled dataset (Table 2). However, the intercepts andslopes of the linear relationship varied with rice cultivars, these differences might be partly
caused by the different plant type among cultivars, with a loose shape and large mean leaf
angle in hybrid indica rice (Liangyoupeijiu) [40,41] while tight shape and small mean leaf
angle in japonica rice (Nanjing45, Nanjing46). The different plant type lead to different
distribution of reflectance [42,43] and finally caused different image-derived indices.
Figure 2 Relationships between leaf nitrogen concentration (LNC, g kg−1) and image
color index b* in vegetative (a), tillering (b), jointing (c), booting (d) stages, and thepooled data of the four stages (e) in 2011.
Figure 3 Relationships between SPAD readings and image color index b* in vegetative(a), tillering (b), jointing (c), booting (d) stages, and the pooled data of the four stages (e)in 2011.

Table 2 Statistics of the linear regression analysis between color index b* and SPAD
readings in different development stages and cultivars in 2011
R2

Liangyoupeijiu

RMSEa
R2
RMSE
R2
RMSE
R2
RMSE

Nanjing45
Nanjing46
All cultivars

Vegetative
0.74**

Tillering
0.62**

Jointing
0.92**

Booting
0.86**All stages
0.80**

0.87
0.42**
1.61
0.83**
0.94
0.31**
1.94

1.18
0.28**
1.41
0.52**
1.62
0.64**
1.66

0.86
0.90**
0.64
0.82**
1.47
0.65**
1.87

1.23
0.79**
0.91
0.83**
1.26
0.61**
2.59

1.24
0.60**
1.60
0.59**
1.85
0.53**
2.23

Dependent variable: SPAD.
**
indicate the significance at P < 0.01.
aRoot mean square error.

In our experiments, image acquisition was carried out in the field under natural light nearsolar noon which was the period with the most stable illumination at the top of theatmosphere. This makes sure that the light intensity is not changing too much during image
acquisition in a single day. The results from Table 2 indicated that reliable estimates of N
status could be obtained from images taken under natural light. Considering the stability of N
diagnosis in different locations and sampling dates, individual sampling dates could not meetthe needs of crop monitoring and N diagnosis in various environmental conditions. Therefore,
regression analysis was carried out with the pooled data of sampling dates. There were large
differences of light intensity among different sampling dates even under overcast days (Table
3, PAR ranging from 145 to 692 µmol m−2 s−1 in 2011).
Table 3 Rice cultivars, sampling dates (indicated as days after transplanting, DAT),
photosynthetic active radiation (PAR, µmol m−2 s−1) and the number of samples in thetwo experiments
Experiment

Year

Cultivar

Vegetative

Tillering

Jointing

Booting

Number of samples

I

2010
2011
2010

Wuyunjing24
Nanjing46
Nanjing44
Yangjing48
Nanjing45
Liangyoupeijiu

25a (937)b18 (145)
22 (1215)
22 (1449)
16 (427)
16 (427)

36 (1536)
35 (692)
35 (1058)
35 (1058)
29 (654)
29 (654)50 (1369)
55 (203)49 (1759)
49 (1759)
51c (589)
51 (589)

64 (1532)
75 (296)
63 (1477)
63 (1477)
57 (289)
65 (621)

18
24
18
18
18
18

II

2011
a

represents sampling dates (the days after transplanting, DAT).
represents the average PAR during the period of image acquisition.
cBecause of the continuous sunny days in the jointing stage for Nanjing45, sampling dates was delayed about a week.
b

Interestingly, regression analysis did not show any evidence that the relationship between
SPAD and color index b* was affected by the varying light intensity (Figure 3e). This might
be attributed to the auto exposure controlled by the digital camera which adjusted theexposure time to make compensation for the amount of light reaching the image sensor.
However, there were no significant trends between LNC and SPAD readings, or between
LNC and color index b* using data pooled across different sampling dates (Figures 1e and2e). Previous studies revealed that, for rice and corn, the relationship between LNC andSPAD readings could be improved simply by dividing the readings with specific leaf weight(SLW = dry leaf weight/leaf area) of the sampled leaves or introducing SLW as a second
independent variable in the multiple regression [44-46]. The reason is that SPAD readings
vary with leaf thickness which can be different in cultivars, developmental stages andenvironmental conditions [46,47], while the LNC has a relatively consistent value. The
uncertain relationship between LNC and color index b* (Figure 2e) may also be caused by thedifference of leaf thickness, because the color index b* and SPAD readings both reveal thespectral information of leaves, and their relationship keeps consistent with the pooled data ofdifferent sampling dates (Table 2 and Figure 3e). Nowadays, destructive sampling or
hyperspectral-reflectance [48] is required for the measurement of SLW, however, including
this defeats the purpose of using a cheap and simple digital camera.

Model calibration and validation
Since developmental stages in rice affected the response of N parameters to color indices,multiple linear regression analysis was carried out with the consideration of days after
transplanting (DAT) to estimate LNC and SPAD (Table 4). The multiple linear models highly
improved the R2 of SPAD readings and b* with LNC (Table 4, Figure 4a and b). The
relationships between SPAD readings and b* were less affected by the process ofdevelopment for all the cultivars (Table 2 and Figure 3e). Therefore, the consideration of
DAT in the multiple linear regression did not improve the R2 effectively (Figure 4c). The
slope of the regression lines in Figure 4 was all less than 1, which indicated that predicted
LNC or SPAD were generally smaller at the high value area and bigger at the low value area,
than the observed ones. It was noteworthy that most of the predicted LNC in the vegetative
stage (red symbols) were underestimated in Figure 4a and b. This is mainly because that theplants at this stage has higher concentration of N but lower concentration of chlorophyll. In
this case, the lower concentration of chlorophyll would cause an underestimation of the LNC
in the vegetative stage.
Table 4 Statistics of the calibration and validation results for estimating leaf nitrogenconcentration (LNC, g kg−1) and SPAD with color index b*, SPAD and days after
transplanting (DAT)
ModelsCalibration
Model 1 LNC = αSPAD + βDAT + γ
Model 2 LNC = αb* + βDAT + γ
Model 3 SPAD = αb* + βDAT + γ
Model 4 SPAD = αb* + γ
Validation
Model 1Model 2Model 3Model 4

α

β

γ

RMSEa

R2

1.02 ± 0.07
−0.67 ± 0.06
−0.60 ± 0.04
−0.60 ± 0.04

−0.31 ± 0.01
−0.29 ± 0.01
0.024 ± 0.007

7.01 ± 2.95
69.27 ± 2.10
59.66 ± 1.14
60.70 ± 1.12

3.07 g kg−1
3.36 g kg−1
1.62
1.62

0.78
0.71
0.55
0.53

2.43 g kg−1
2.59 g kg−1
2.01
1.89

0.75
0.62
0.46
0.47

NMBb

1.19%
−1.32%−1.94%−2.00%The dataset in 2011 was used for model calibration and the dataset in 2010 used for model validation.
aRoot mean square error.
bNormalized mean bias.

Figure 4 Calibration of the four models in Table 4 for the estimation of leaf nitrogenconcentration (LNC, g kg−1) and SPAD. Model 1 (a), LNC = αSPAD + βDAT + γ, model 2(b), LNC = αb* + βDAT + γ, model 3 (c), SPAD = αb* + βDAT + γ, model 4 (d), SPAD = αb*+ γ. Different colors denote different developmental stages (red: vegetative, green: tillering,
blue: jointing, purple: booting). Different symbols denote different cultivars used for model
calibration (○ Liangyoupeijiu, ∆ Nanjing45, □ Nanjing46).
Validations were performed on the four models in Table 4 with all data obtained in 2010
under sunny days. The images used in model calibration and validation were taken under
different weather conditions, the objective of this combination was to evaluate whether themodel was robust under different light conditions. In general, good performances on thepredicted models were observed for the estimation of LNC and SPAD (Figure 5). Model 1
showed the best performance on the prediction of LNC with a normalized mean bias of1.19% (Table 4 and Figure 5a). Model 2, 3 and 4 showed relatively lower R2 and smaller
negative bias. The model for the prediction of LNC with color index b* (Figure 5b) was notseverely affected by the different light conditions in 2010 and 2011, compared to theprediction of LNC with SPAD (Figure 5a). As with the calibration results, most of the data inthe vegetative stage were below the 1:1 line in models 1 and 2. In addition, the data in
tillering stage deviated from the 1:1 line in models 3 and 4, which resulted in the low R2(Figure 5). The similar R2 and RMSE in Figure 5c and d indicated that the relationshipbetween SPAD and color index b* was not affected by the developmental stage.
Figure 5 Validation of the four models using the fitted parameters in Table 4 for theestimation of leaf nitrogen concentration (LNC, g kg−1) and SPAD. Model 1 (a), LNC =αSPAD + βDAT + γ, model 2 (b), LNC = αb* + βDAT + γ, model 3 (c), SPAD = αb* + βDAT+ γ, model 4 (d), SPAD = αb* + γ. Different colors denote different developmental stages(red: vegetative, green: tillering, blue: jointing, purple: booting). Different symbols denote
different cultivars used for model validation (○ Nanjing44, ∆ Wuyunjing24, □ Yangjing48).
In our study, the light conditions during image acquisition were different between 2010 and2011. Image acquisition in 2011 was under overcast days with low but stable diffused light.
In contrast, images were taken under sunny days in 2010 with strong and variable (PARranging from 937 to 1759 µmol m−2 s−1) light (Table 3). We can remove the effect ofillumination change on images when the change is over the whole image by normalizing theimage [49] or using a calibration panel [36]. However, there were many other differences
caused by the different light conditions. The strong illumination in 2010 caused many whitespots and shadows which affected the image color (Additional file 1: Figure S1) while thisphenomenon was hardly seen in the images taken in 2011. With the rice growth, the lightstatus within rice canopy (transmittance, reflectance, absorption) becoming more and more
complex, the change of illumination will cause different degrees of influence on canopies thatin different height (Additional file 2: Figure S2). In this case, it is difficult to calibrate theimage color accurately. Sakamoto et al. [15] calibrated image indices with the introduction of
exposure value (EV) and obtained reliable camera retrieved vegetation indices (VIs).
However, this method cannot apply to our study for the calibration of canopy color becauseof the different influence in one image.
Besides the light conditions, the prediction of crop N status with leaf color may be affectedby many other environmental factors, such as developmental stage, diseases and drought
stress [50]. These factors may be detectable from high-resolution canopy images [22,23].
Color indices analysis associated with these factors and other image characteristics (e.g.
canopy cover, plant shape, leaf texture or even soil status), will provide more reliable results
to N diagnosis. Further studies will be devoted to the exploration of image characteristics,
leaf color correction and the calibration of the established model with physiologicalparameters (e.g. SLW) in the evaluation of N status under different environmental conditions.

Conclusions
Image color indices calculated from RGB, HSV and L*a*b* color models have significant
correlations with SPAD readings and leaf N concentration (LNC) of rice leaves. Among thesecolor indices, the index b*, which represents the visual perception of yellow-blue chroma, had
the highest correlation coefficients with SPAD readings and LNC. Regression analysis
showed significant linear relationships between index b* and N parameters. However, therelationship between LNC and SPAD reading, LNC and index b* were affected by the ricedevelopmental stage. This is mainly caused by the leaf thickness which can be different incultivars, developmental stages and environmental conditions. In this case, linear regression
models were established between color index b*, LNC and SPAD readings by considering thedevelopmental process in rice. The multiple linear models improved the R2 of SPAD readings
and b* with LNC, yet most of the predicted LNC in the vegetative stage were underestimatedbecause of the inconsistent relationship between chlorophyll and N concentration.
Validations on the models showed good performance and acceptable predicted precision withdifferent cultivars and sampling dates under different natural light conditions. These results
indicated that digital color image analysis could be a simple method for assessing rice N
status under natural light conditions.

Materials and methods
General information of the experimental site
The experiment was laid out at Changshu Agricultural Ecology Experiment Station,
Changshu, Jiangsu, China (31°33′N, 120°42′E). Located in the humid subtropical climate
zone, the station receives average annual solar radiation of 4930 MJ m−2, sunshine of 1800
hours, precipitation of 1200 mm and cumulative temperature above 10°C of 4933 degreedays (°C·d). The soil type for the field experimental site is a gleyed paddy soil of the Taihu
Lake region, which contains total nitrogen (N) of 1.79 g kg−1, total phosphorus (P) of 0.93 g
kg−1, total potassium (K) of 18.7 g kg−1, organic matter of 30.8 g kg−1, alkali-extractable N of
123 mg kg−1, Olsen-P of 13.1 mg kg−1, plant available K of 121 mg kg−1 and pH of 7.4 (soil:
water, 1:2) in the 0–15 cm soil layer.

Experimental design
Two independent experiments with different N fertilization gradients were implemented inour study. Experiment I was a long-term site-specific rice-wheat rotation experiment thatstarted in 1997. The trial comprised six fertilizer treatments represented as CK, N0, N1, N2,
N3, and N4 for N application of 0, 0, 180, 225, 270 and 315 kg N ha−1 in rice season,
respectively. Each treatment had four replicates which were arranged in a randomized block
design. The data used in this paper were from the period May to November in 2010 and 2011
with cultivars Wuyunjing24 and Nanjing46, respectively. Experiment II was carried out inpaddy fields with a rice-wheat rotation in 2010 and 2011. Six N application rates with three
replicates were designed in this trial, which were represented as N0, N1, N2, N3, N4 and N5
with N application of 0, 120, 180, 240, 270 and 300 kg N ha−1, respectively. The cultivars
were Nanjing44 and Yangjing48 in 2010, and Nanjing45 and Liangyoupeijiu in 2011. For
both experiments, the N was split into three applications, 40% as basal, 20% at tillering and
40% at booting. In addition, each plot received 90 kg K ha−1 and 20 kg P ha−1 except the CK
treatment in experiment I. The applied K was split into 50% as basal and 50% at booting, andall the P was applied as basal fertilizer. Other crop managements were same as the local
traditional practices.

Sample collection and digital image acquisition
For measuring rice growth and nutrition parameters, the above-ground part of rice plant was
sampled about every two weeks after transplanting until the booting stage. A total of 4 sets ofsamples were collected in 2010 and 2011 (Table 3). The plant samples were separated into
leaves and stems (including sheaths), and dried at 105°C for half an hour and then at 70°C
until constant weight. After that, the samples were weighed for dry weight and analyzed forleaf N concentration (LNC) by the Kjeldahl method [51]. Along with the plant sampling, achlorophyll meter (SPAD-502, Minolta Camera Co., Osaka, Japan) was used to obtain SPAD
values on the four youngest fully expanded leaves. Each blade was measured at three points:on the upper, middle and lower thirds on either side of the midrib. Then, average SPAD
readings were calculated for each plot.
On the same day or following day of plant sampling, images of the rice canopy were captured
using a digital still color camera (EOS 50D, Canon Inc.) with a resolution of 15 mega pixels.
The camera was mounted on a tripod at the nadir position with a constant height of 1 m above
the top of the rice canopy. Aperture priority mode was selected, and the camera was set at
aperture of f/5.6, ISO of 100, white balance of 4,900 K, auto exposure and auto-focus withthe flash turned off. In 2010, the pictures were taken at local time 12:00 – 13:00 in sunnydays, while in 2011, the pictures were taken at the same time period but on overcast days. In
the days of picture taken (July and August), the deviation between local time and solar noon
was within 4 minutes. All the pictures from the experiments were stored in CR2 (Canon raw
image file) format. The photosynthetic active radiation (PAR) and illuminance were recorded
by a portable light meter (GLZ-C, Top Instrument Co., Zhejiang, P. R. China) during theperiod of image acquisition. Average PAR was calculated with each set of pictures, andobserved 937–1759 µmol photons m−2 s−1 and 145–692 µmol photons m−2 s−1 in 2010 and2011, respectively (Table 3).

Image segmentation and color indices calculation
A raw image file contains minimally processed data from the image sensor of a digital
camera. This file saves settings of white balance, color saturation, contrast and sharpness init, but defers the processing. Therefore, all the modification made on a raw image file is nondestructive.
The canopy images in CR2 format were adjusted for white balance using the 18% gray card
(R-27, Kodak) pictures which were taken simultaneously with the canopy images. Then, lensdistortion correction was applied, and exposure was set to +1 for all images. After that,images were saved as joint photographic experts group (JPEG) files for further processing.
All the procedures above were processed with Adobe Camera Raw (Adobe Systems Inc.).
Since the images contained the rice canopy and some non-canopy elements, such as soil,
water and plant residues, images were segmented into canopy portion and non-canopy
portion. A computer program was developed based on the G-R thresholding method [25,29]
using MatLab® (MathWorks Inc.) to extract the canopy portion of the image. The G-R
thresholding method was proposed according to the difference of reflectance spectrum
between green vegetation and non-canopy elements in the visible band. There is a reflection
peak for green vegetation in the green band, whereas no apparent change for soil or water
albedo in the whole visible band. Therefore, the value of green channel minus that of red
channel expands the difference between canopy and non-canopy portion.
After the image segmentation, 13 color indices derived from 3 color models were calculated.
RGB model is the most common color model for the representation of digital images. A color
in the RGB model is described by indicating how much of each of the red, green, and blue isincluded. The color is expressed as an RGB triplet (R, G, B), with the representation for blackof (0, 0, 0) and for the brightest representable white of (255, 255, 255) in an 8-bit image [52].
R, G and B are the mean values of the red, green and blue channels, and r, g and b are thenormalized RGB values, respectively. Intensity (INT) is the average of R, G and B. VIGreen isa widely used vegetation index [13]. These indices were calculated as follows [27,29]:

r = R / ( R + G + B)

(1)

g = G / ( R + G + B)(2)

b = B / ( R + G + B)

(3)

INT = ( R + G + B ) / 3(4)

VI Green = ( G − R ) / ( G + R )(5)

In addition, the CIE L*a*b* and HSV color spaces were also tested in this study. The L*
coordinate in CIE L*a*b* [53] closely matches human perception of lightness, a* and b*
dimensions represent the visual perception of red-green and yellow-blue chroma,
respectively. Both a* and b* are independent with image lightness (L*), and take on both
negative and positive values (+a* reds, − a* greens, + b* yellows, − b* blues). The threecoordinates of L*a*b* are computed from the tristimulus values X, Y and Z as following
equations [32,54]:
L∗ = 116 f (Y Yn ) − 16(6)

a = 500  f ( X X n ) − f (Y Yn ) (7)

b = 200  f (Y Yn ) − f ( Z Z n ) 

(8)

∗

∗


(ω )
f (ω ) = 
7.787 (ω ) + 16 116
13ω < 0.008856
ω ≥ 0.008856(9)where Xn, Yn and Zn describe a specified white object-color stimulus.
The HSV color space is represented as a cylindrical-coordinate in which the angle around thecentral vertical axis corresponds to hue (H). The calculation of H was listed below [27,29]:{}

 60 ∗ ( G − B ) /  max ( RGB ) − min ( RGB )  , max ( RGB ) = R
H = 60 ∗ 2 + ( B − R ) /  max ( RGB ) − min ( RGB )  , max ( RGB ) = G60 ∗ 4 + ( R − G ) /  max ( RGB ) − min ( RGB )  , max ( RGB ) = B

(10)

{ {
{ {

}}
}}

Pearson correlation and regression analyses were used to detect the relationship between
color indices and crop N status. The significance of linear regressions was evaluated using
Student’s t-test at 95% confidence levels. Significance of ANOVAs was evaluated with theleast significant difference test (LSD) at 0.05 probability level. Data analysis and figure
production were done using the R v3.0.3 software [55].

Correlation analysis, linear regression analysis and model establishment between color
indices and crop N status were based on the data in 2011, and the data in 2010 were used formodel validation.

Abbreviations
N, Nitrogen; LNC, Leaf nitrogen concentration; H, Hue from HSV color space; R, G, B,
Digital number for the red, green and blue channel of an RGB image; r, g, b, Normalized
RGB values; INT, The average of R, G and B.

Competing interests
The authors declare that they have no competing interests.

Authors’ contributions
YW and DW designed the study. YW and PS implemented the experiments. YW, PS and KO
performed the statistical analysis. YW, DW, PS and KO drafted the manuscript. All authors
read and approved the final manuscript.

Acknowledgements
This research was supported by the National Key Technologies R & D Program of China(2012BAJ24B06). The authors acknowledge John Angus of CSIRO Agriculture for his
constructive comments and revisions of the manuscript. We wish to thank the editor and two
anonymous reviewers for their very helpful comments on earlier drafts.

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Additional files
Additional_file_1 as PDF
Additional file 1: Figure S1 Examples of the “white spots”, which are over-exposed areas
where the reflected light came into the camera directly.

Additional_file_2 as PDF
Additional file 2: Figure S2 Canopy images of Nanjing46 in different developmental stages(a, vegetative; b, tillering; c, jointing; d, booting).

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Additional files provided with this submission:
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http://www.plantmethods.com/imedia/1320963668145391/supp1.pdf
Additional file 2: 2039600106133445_add2.pdf, 1092K
http://www.plantmethods.com/imedia/1081465145391091/supp2.pdf

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