Munsell Soil Color Chart Download Pdf

Soil colour has been determined in most cases by using Munsell soil-colour charts, sometimes with spectrometers, and occasionally with digital cameras. The objective here is to assess whether a mobile phone, which has all the requirements to capture and process digital images, might also be able to provide an objective evaluation of soil colour under controlled illumination. For this, we have developed an Android application that takes a picture of a soil sample, allowing the user to select the region of interest and then, after a RGB image-processing and a polynomial process transform between colour spaces, the Munsell (HVC) and CIE (XYZ) coordinates appear on the screen of mobile phone. In this way, a commercial HTC smartphone estimated the colour of 60 crumbled soil samples between 2.9YR and 2.3Y with a mean error of 3.75 ± 1.81 CIELAB units, taking as a reference the colour measurements performed with a spectroradiometer. The Munsell hue had the worst estimates (mean error of 2.72 ± 1.61 Munsell units) because of its geometric mismatch with the RGB colour space and for being defined to illuminant C, different of the D65 source under which the phone camera took the pictures. Because the measuring errors were lower than those described in the literature for the visual determination of soil colour, and the application also worked successfully in a different smartphone than the one used in its development, we think that current experimental results encourage the expectations of using smartphones in the field as soil-colour sensors.

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Using the mobile phone as Munsell soil-colour sensor: An experiment

under controlled illumination conditions

Luis Gómez-Robledo

⇑

, Nuria López-Ruiz

b, 1

, Manuel Melgosa

, Alberto J. Palma

Luis Fermín Capitán-Vallvey

, Manuel Sánchez-Marañón

Departamento de Óptica, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain

ECsens, Departamento de Electrónica y Tecnología de Computadores, ETSIIT, Universidad de Granada, 18071 Granada, Spain

Grupo de Investigación Espectrometría en Fase Sólida, Departamento de Química Analítica, Facultad de Ciencia, Universidad de Granada, 18071 Granada, Spain

Departamento de Edafología y Química Agrícola, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain

article info

Article history:

Received 30 January 2013

Received in revised form 24 July 2013

Accepted 2 October 2013

Keywords:

Android

Colorimetry

Digital camera

Munsell soil-colour charts

abstract

Soil colour has been determined in most cases by using Munsell soil-colour charts, sometimes with spec-

trometers, and occasionally with digital cameras. The objective here is to assess whether a mobile phone,

which has all the requirements to capture and process digital images, might also be able to provide an

objective evaluation of soil colour under controlled illumination. For this, we have developed an Android

application that takes a picture of a soil sample, allowing the user to select the region of interest and then,

after a RGB image-processing and a polynomial process transform between colour spaces, the Munsell

(HVC ) and CIE (XYZ ) coordinates appear on the screen of mobile phone. In this way, a commercial HTC

smartphone estimated the colour of 60 crumbled soil samples between 2.9YR and 2.3Y with a mean error

of 3.75 ± 1.81 CIELAB units, taking as a reference the colour measurements performed with a spectrora-

diometer. The Munsell hue had the worst estimates (mean error of 2.72 ± 1.61 Munsell units) because of

its geometric mismatch with the RGB colour space and for being deﬁned to illuminant C , different of the

D65 source under which the phone camera took the pictures. Because the measuring errors were lower

than those described in the literature for the visual determination of soil colour, and the application also

worked successfully in a different smartphone than the one used in its development, we think that cur-

rent experimental results encourage the expectations of using smartphones in the ﬁeld as soil-colour

sensors.

1. Introduction

Specifying colour by the Munsell system is usual for artists,

designers, scientists, engineers, and government regulators (ASTM,

2008). For example, in the natural sciences, it has been used to

identify and record the colours of specimens such as human skin,

ﬂowers, foliage, minerals, and soils. Speciﬁcally in soil science,

Munsell colour has far-reaching implications for the examination,

description, and classiﬁcation of soils (Soil Survey Staff, 1999; IUSS

Working Group WRB, 2006), as well as in studying soil genesis and

evaluation, being a valuable indicator of soil structure and compo-

nents (Sánchez-Marañón et al., 2004 ). Although the specialized

work in soil colour has been based on instrumental measurements

(Torrent and Barrón, 1993; Viscarra Rossel and Webster, 2011 ), the

prevalent practice in soil science is visual determination. This seeks

the closest match between the soil sample and one of the standard

chips contained in the Munsell soil-colour charts, i.e. artiﬁcially

coloured papers mounted on constant hue charts, showing value

(lightness) and chroma (colour intensity) variations in the vertical

and horizontal directions, respectively. Thus, the Munsell designa-

tion of that chip (hue H , value V , and chroma C ) is assigned to the

soil sample under study. Several accuracy problems, however, have

been reported previously in relation to identifying the colour of soil

specimens using Munsell charts (Sánchez-Marañón et al., 1995,

2005, 2011), all of them related to the three main factors affecting

the psychophysical character of colour: illumination conditions,

sample characteristics, and the observer's sensitivities (Berns,

2000).

Currently, the increasing demand of soil data for applications

such as precision agriculture and dynamic models for monitoring

environmental change has spurred the development of proximal

soil sensors (Viscarra Rossel et al., 2011 ). The rationale is to collect

larger amounts of data using simpler, cheaper, faster, and less labo-

rious techniques than conventional soil analyses, even at the cost

of less accuracy. The point is that many more measurements will

http://dx.doi.org/10.1016/j.compag.2013.10.002

⇑

Corresponding author. Address: Departamento de Óptica, Universidad de

Granada, Cuesta del Hospicio s/n, 18071 Granada, Spain. Tel.: +34 958241903.

E-mail address: luisgrobledo@ugr.es (L. Gómez-Robledo).

Both authors contributed equally to this work.

Computers and Electronics in Agriculture 99 (2013) 200–208

Contents lists available at ScienceDirect

Computers and Electronics in Agriculture

journal homepage: www.elsevier.com/locate/compag

counteract their lower accuracy. Since soil colour is related to soil

components, and, by extension, properties or conditions depending

on them (Soil Survey Staff, 1993 ), proximal soil-colour sensing may

provide an integral way of comparing soils, including evolution,

degradation, pedoclimate, and fertility analyses. Digital cameras

have already been proposed as soil-colour sensors (Aydemir

et al., 2004; Viscarra Rossel et al., 2008; O'Donnell et al., 2011)

due to the possibility of gaining reliable colour information from

RGB digital images. Currently, the only colour measure by pixels

is based on RGB signals, and several authors (Meyer et al., 2004;

Viscarra Rossel et al., 2006; León et al., 2006; Rodriguez-Pulido

et al., 2012) have reported computational solutions that allow dig-

ital images to be transformed into standard colour spaces from

each pixel of the digital RGB image.

The requirements for soil-colour measurement are available or

can be implemented in the current typical mobile phone. This in-

cludes a high-resolution digital camera, with a low-power high-

performance processor at running frequencies of up to 1 GHz,

sophisticated operating systems offering multi-tasking, Java sup-

port, and options for installing and running externally developed

applications. Initially, mobile phones with built-in cameras were

used as imaging devices to collect and transmit digital data to an

off-site laboratory or external computer, which processed the

information and returned the analysis results to the phone. Thus,

mobile phones have been used, for example, for biological and

forensic applications (Cadle et al., 2010 ), telemedicine (Martinez

et al., 2008), and for bad-smell monitoring of the living environ-

ment (Nakamoto et al., 2009 ). However, only recently, has a mobile

phone been used for on-site data processing to determine speciﬁc

analyte concentrations from single-use chemical reactive mem-

branes considering hue changes from blue to magenta (García

et al., 2011).

The potential of the mobile phone suggests that it may answer

the increasing demand of objective soil-colour data. As this elec-

tronic device becomes available for everyone, in contrast to the

more complex and costly colorimeters and spectrometers, its use

as a proximal sensor in the ﬁeld becomes more feasible. However,

never before has a mobile phone been colorimetrically tested to

discriminate among the colour gamut encompassed by the Munsell

soil charts. Thus, against a dichotomous choice of colours (García

et al., 2011), the mobile should now distinguish between different

reddish, brownish, and yellowish hues, from dark to light and of

variable intensity. On the other hand, the few works that have

measured soil colour with digital cameras (e.g. Viscarra Rossel

et al., 2008) needed external software for calculations, when ad

hoc solutions performing the immediate computational conversion

on the same platform that produced the RGB images would be

desirable. Moreover, as outlined in the literatures (Hong et al.,

2001; Westland and Ripamonti, 2004), the RGB colour space is de-

vice-dependent, and we cannot be sure of the effectiveness of stan-

dard transformation equations (Wyszecki and Stiles, 2000 )to

establish colorimetric coordinates from the RGB information. Final-

ly, we also suspect the possible inﬂuence of natural daylight condi-

tions (Sánchez-Marañón et al., 2011 ), which deserves a separate

study. Accordingly, before going to the ﬁeld, laboratory experience

is necessary.

In the present work, our overall goal was to investigate in the

laboratory the potential of a mobile phone (smartphone) to capture

soil-colour images and process them with a colorimetric rationale,

returning the Munsell notations corresponding to the digital RGB

captured images. To do so, our speciﬁc objectives were: (i) to de-

velop a custom image-processing application for mobile phone;

(ii) to build a model and estimate its parameters for establishing

Munsell notations from RGB measurements; (iii) to implement

the image processing and the conversion model RGB ? Munsell,

making them work together as software in the mobile phone;

and (iv) to assess the accuracy achieved by the mobile phone with

respect to that of commercial spectrometers.

2. Materials and methods

2.1. Developing custom image processing for a mobile phone

Although there is a wide range of operating systems for devel-

oping applications in mobile phones such as Android, Symbian,

BlackBerry or Windows Mobile, the former (Android 2.2, Google,

EEUU) was selected here for several reasons. Firstly, Android is well

established in most communication devices, including mobile

phones, netbooks, tablets, and even in electrical appliances such

as microwaves and washing machines (Puder and Antebi, 2013).

Secondly, Android uses an open code and a free license, allowing

a wide community of developers to expand and improve its func-

tionality. Thirdly, Java is the language used by Android, enabling

the use of libraries and other applications previously developed

for this language.

Our software was designed to read colour information from dig-

ital images in JPEG format captured with the built-in camera of a

smartphone and to carry out processes of colorimetric analysis.

The smartphone HTC Desire HD (HTC Corporation, Taiwan),

4.84

2.68

0.46

in size and 164 g in weight, has the necessary

computing power to run the program, also including a 4.3

touch-

sensitive screen with a resolution of 800  480 pixels and an inte-

grated camera up to 8 megapixels with CMOS autofocus sensor. For

the device setting, we ﬁxed the ISO parameter (sensitivity of the

image sensor to light) to 100, according to the daylight illumina-

tion conditions (6500 K), the white balance in the daylight option

for high luminance (1580 lx), and the ﬂash in the off position.

The smartphone saved the picture in JPEG format with a size of

1952  3264 pixels, a weight of around 1 MB, a resolution of 72

dots per inch, and using 8-bits per channel (24 bits in total for

the red, green, and blue channels).

Once our application is installed in the mobile phone the user

interface on the screen may serve to show all the functionalities

(Fig. 1 ). At program startup, a main menu shows four options

(Fig. 1 a). To normalize any colour to a white, in accordance with

colorimetry (CIE, 2004 ), a calibration option was included to deter-

mine the RGB values from the photo of a reference white (Konica

Minolta PTFE, Fig. 1 b and c). A square frame over the picture could

be moved, expanded, or contracted, in order to select the region of

interest to process. The colour information of each pixel inside the

cropped area was directly read by the phone, our program giving

the statistical mode for R , G , and B . For an analysis of a heteroge-

neous colour image, the mode has proved more suitable than the

mean value (García et al., 2011 ), removing the undesirable effect

of noisy pixels. The same RGB information could be extracted from

each picture of colour samples (Fig. 1 d and e), which, after being

normalized with the reference-white coordinates (Fig. 1 c), can be

shown and saved in the phone (Fig. 1 f). This normalization was

made for each pixel following Eq. (1), where n (8 in our case) rep-

resented the number of bits per pixel for each colour channel

(Salmeron et al., 2012).

RGB

normalized

¼2

RGB

acquired

=RGB

white

ð1Þ

2.2. Transformation equations from RGB to XYZ and HVC

As shown in Fig. 1 f, we wanted the mobile-phone application to

provide not only RGB

normalized

(henceforth RGB ) values, but also the

colour designation in the CIE (XYZ ) and Munsell (HVC ) systems.

While the Munsell parameters are widely used in soil science,

the tristimulus values XYZ are the basis of instrumental colorime-

L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208 201

try. The two possible solutions to ﬁnd XYZ and HVC from the origi-

nal RGB were either standard transformation equations as those

collected by Viscarra Rossel et al. (2006) or empirical equations

established from our colour measurements.

The standard equations were processed by using the ColoSol

software (Viscarra Rossel, 2006 ) while, for developing empirical

equations, we used the colour measurements of 238 chips from a

recent edition of Munsell soil-colour charts (Munsell Color Com-

pany, 2000) which were completely new. The measurements of

each chip were undertaken with the HTC smartphone running

our image-processing application for determining its RGB vari-

ables, as well as with a spectroradiometer Konica Minolta

CS2000 (Tokyo, Japan) for registering the reﬂected spectral-power

distribution between 380 and 780 nm at 2 nm steps. For calculat-

ing the XYZ tristimulus values, we assumed the CIE 1964 Standard

Observer (CIE, 2004 ). In a dark room, the chips of each Munsell

Fig. 1. Screen of the HTC smartphone running the Android app for measuring soil colour: (a) main menu; (b) calibration with a reference white; (c) RGB coordinates of the

reference white; (d) selection of a region of interest for one chip of the Munsel soil colour charts using a square frame; (e) a picture with a soil sample; and (f) colour

coordinates of a soil sample.

Fig. 2. Measuring process: (a) geometry, and (b) a photograph under the experimental conditions with a Munsell chart and the reference white.

202 L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208

chart together with the reference white were placed inside a Gre-

tagMacbeth Spectralight III lighting booth (X-Rite, Switzerland)

equipped with a D65 simulator (Fig. 2 ), in agreement with the

standard ASTM (2008) for the Munsell colour determination. We

ﬁxed the position of smartphone and spectroradiometer and

moved the Munsell chart to focus the image on the centre of each

chip. Three replicates of each measure were taken.

Using the measured variables RGB and XYZ , in addition to the

HVC variables taken from the notation of each chip in the Munsell

charts, we computed polynomial transformations between colour

spaces (Johnson, 1996 ) by the pseudo inverse method (Penrose,

1955), in order to build statistical prediction models of XYZ and

HVC from RGB. In the matrix relation i= Td, where i (from indepen-

dent device colour) is XYZ or HVC (3  1 matrix), d (from dependent

device colour) is an RGB n  1 matrix, and we calculated the Tma-

trix of dimension 3 n (n = number of polynomial coefﬁcients)

which provided the nearest relationship between vectors i and d.

These transformations were made by using Matlab2009b (Math-

works, EEUU) and following the steps suggested by Westland and

Ripamonti (2004). The partition of Munsell hue for the calculations

was 10 for 10R, 12.5 for 2.5YR, and so forth up to 22.5 for 2.5Y, and

25 for 5Y. In addition to statistical coefﬁcients, the accuracy of the

models was examined by the differences between measured and

predicted values using the CIELAB colour-difference formula



(CIE, 2004 ) and the Munsell colour-difference formula

(God-

love, 1951), depending on whether RGB was transformed to the

CIE XYZ or Munsell colour systems, respectively.

2.3. Validation of the mobile-phone application

To assess the accuracy of mobile-phone application for colour

sensing in different samples from those used to develop it, we em-

ployed new soil-colour objects and an increasingly stringent vali-

dation plan. First, we selected colour samples from the Natural

Colour System (NCS, Sweden) Atlas covering the complete colour

gamut encompassed in the Munsell soil-colour charts. Although

the characteristics of these colour samples were similar to those

of the chips of Munsell soil-colour charts, i.e. pieces of artiﬁcially

coloured plain paper, they were made with different pigments

and the resulting colours did not match those of the Munsell chips

but rather matched intermediate colours. The selection process

among available 1950 NCS colours was initially visual, followed

by measuring each of the visually selected ones (100 samples) with

a Konica Minolta 2600d spectrophotometer (Tokyo, Japan) to

quantify the Munsell colour. This instrument has an illuminating/

viewing geometry d /8 and two Xenon lamps as the light source,

making measurements of the light reﬂected by the specimen sur-

face with the specular component excluded between 360 and

740 nm at 10 nm intervals. Among the colour indices in the output

of display, this instrument provided the Munsell colour parameters

HVC. The ﬁnal selection consisted of 40 NCS samples which Mun-

sell colour parameters ranged between 0.1YR and 5.5Y in hue, be-

tween 2.8/ and 8.2/ in value, and between /0.5 and /8 in chroma, all

these colours being approximately homogenously distributed over

the colour space corresponding to the seven Munsell soil-colour

charts.

Subsequently, we tested the application in soil samples. Like the

NCS samples, the colours of our soil samples were within the space

of Munsell soil-colour charts, but now we managed textured and

heterogeneous colour samples, which implied a more challenging

test for the mobile phone. To cover the widest possible soil-colour

range of our pedogenic environment, we inspected the soil samples

gathered in our laboratories. Finally, 45 samples were chosen, all

from Mediterranean soils with different degrees of development

such as Entisols, Inceptisols, Vertisols, and Alﬁsols. These samples

had also been used previously in colorimetric studies to evaluate

the effects of illumination, sample state, and observer on the

soil-colour determination by Munsell charts (Sánchez-Marañón

et al., 1995, 2005, 2011). As usual in laboratory colour studies (Tor-

rent and Barrón, 1993), we prepared air-dried samples of ﬁne earth

(soil particles < 2 mm) for the 45 study cases plus 15 samples taken

from the same group but grinding and homogenizing them in an

agate mortar until we obtained a powder with particle sizes of less

than 50

m. After the powder was placed in circular plastic con-

tainers (15 mm in diameter and 5 mm thick) with the upper sur-

face open and levelled, their spectrophotometric colour was

measured using the Konica Minolta 2600d spectrophotometer,

the results ranging from 2.9YR to 2.3Y in hue, 3.8 to 7.0 in value,

and 1.9 to 5.6 in chroma.

All colour samples for the validation process were also mea-

sured with the HTC smartphone and spectroradiometer under

the same experimental conditions described in Fig. 2 . In addition,

the colour of soil samples was also tested with another mobile

phone, a Samsung Galaxy S2 (Samsung, South Korea) smartphone.

Table 1

Polynomial models for transforming RGB to XYZ and HVC . The r , DE 

ab, and

are, respectively, Pearson's correlation coefﬁcient, mean CIELAB and mean Godlove colour

difference between measured and predicted values in the 238 chips of Munsell soil-colour charts.

Terms of the polynomial r for XYZ models r for HVC models



ab D



[1,R,G,B] 0.9811 0.9090 9.65 2.03

[1,R,G,B,RGB] 0.9943 0.9101 6.03 1.53

[1,R,G,B,RG,RB,GB] 0.9965 0.9246 3.44 1.12

[1,R,G,B,RG,RB,GB,RGB] 0.9965 0.9259 3.39 1.12

[1,R,G,B,RG,RB,GB,R

] 0.9973 0.9547 2.14 1.03

[1,R,G,B,RG,RB,GB,R

,RGB] 0.9973 0.9562 2.07 1.02

[1,R,G,B,RG,RB,GB,R

,RGB,R

] 0.9974 0.9568 1.85 1.02

[1,R,G,B,RG,RB,GB,R

,RGB,R

G,G

B,B

R,R

B,G

R,B

G,R

GB,RG

B,RGB

] 0.9980 0.9695 1.75 0.97

[1,G,B,RG,R

,RGB,B

] 0.9972 0.9407 2.03 1.08

Table 2

Coefﬁcients of the polynomial statistical models / ðRGB Þ¼a þ bG þ cB þ dRG þ eR 2 þfG 2 þgRGB þhB 3 built to compute XYZ and HVC from RGB.

abcd e f g h

X1.3502 0.0759  0.0082 1.05E 04 0.000381 0.000124 2.79E 06  1.31E06

Y0.7246 0.1146  0.0172  3.20E 04 3.06E 04 0.000604 3.08E 06  1.54E06

Z2.1291 0.1028 0.0879 4.80E04 1.04E04 0.000174 2.41 E06 2.13E06

H10.5649 0.4622  0.2192  2.54E 03 1.92E 04 6.09E 04 7.98E 06  4.64E06

V2.3252 0.0303  0.0069  5.36E 05 4.33E 05  1.61E 05 2.40E 07  2.96E08

C1.8311 0.0157  0.0351  3.80E 04 2.89E 04 0.000163  3.00E 07 2.25E07

L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208 203

This phone has a dual core processor and runs the version Android

4.1.2. The built-in camera has also 8 megapixels and it was used

setting equals parameters that described before for the HTC smart-

phone except the resolution, which in this case was of

3264  2448 pixels, increasing the weight of each picture to

2 MB. In this way, the same Android application was installed

and used by a device other than the one used in the design, in order

to compute again the soil colours under laboratory conditions. All

colour measurements were replicated three times to assess their

reproducibility.

3. Results and discussion

3.1. Transformation equations from RGB to XYZ and HVC

Once the image processing worked as expected in the design

(Fig. 1 ), the following major hurdle was to transform the RGB sig-

nals of the mobile phone to CIE XYZ and Munsell HVC colour

0 20 40 60 80 100

100

Polynomial

Colosol

X- Computed

X- Spectroradiometer

0 5 10 15 20 25 30 35

Polynomial

Colosol

H - Computed

H - Munsell soil charts

0 20 40 60 80 100

100

Polynomial

Colosol

Y - Computed

Y- Spectroradiometer

02468

8Polynomial

Colosol

V - Computed

V- Munsell soil charts

0 20 40 60 80 100

100

Polynomial

Colosol

Z - Computed

Z- Spectroradiometer

02468101214

Polynomial

Colosol

C - Computed

C - Munsell soil charts

Fig. 3. Colour coordinates (XYZ and HVC ) measured vs . computed from the RGB signals of HTC smartphone using empirical polynomial transformations and ColoSol software

(n = 238 chips of the Munsell soil-colour charts).

0 50 100 150 200 250

100

150

200

250

RGB mobile

RGB from XYZ

Fig. 4. RGB coordinates calculated with ColoSol from XYZ-spectroradiometric

measurements vs . RGB coordinates measured with the HTC smartphone.

204 L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208

coordinates. In addition to the simplest solution, i.e. standard col-

our-space transformations available in the colour literature (e.g.

Viscarra Rossel et al., 2006), we also tested statistical transforma-

tion models developed from the data measured in the laboratory

using the Munsell soil-colour charts. Table 1 shows some of these

statistical models, from a model of order one with four terms to a

polynomial complex dependence of order three and 23 terms,

although we found that cubic models with eight coefﬁcients (last

row in Table 1 ) optimised the computational work to be made by

the smartphone providing a high correlation and relatively low col-

our differences between measured and predicted values. Table 2

lists the coefﬁcients of the two cubic polynomial transformation

equations forming matrices of dimension 3  8. For the calcula-

tions, we used the mean of three replicated measures on each sam-

ple, although its dispersion (standard deviation) was negligible

both for RBG (<0.01) and XYZ (<0.03) because of the stability of

the light source and homogeneity of the measured surface.

The polynomial transformations proved slightly better than the

standard transformations using the ColoSol software (Fig. 3 ). This

latter underestimated the colour coordinates, except Munsell C,

which was overestimated, while Z and H were the worst estimated

parameters. This may be because the standard transformation

matrices were developed for different conditions (Wyszecki and

Stiles, 2000) from those used here, the RGB values of a same colour

might change because of their device dependence (Hong et al.,

2001; Westland and Ripamonti, 2004) or the inﬂuence of light

intensity (Viscarra Rossel et al., 2008 ), and the standard linear

transformations could not exactly match that of real cameras. Cer-

tainly, the RGB measured from the mobile phone and the RGB lin-

early transformed with ColoSol from XYZ of the Munsell chips

showed a similar relation (Fig. 4 ) to the typical curve for image de-

vices, indicating a non-linear response of the sensor (Westland and

Ripamonti, 2004). Whatever the reason, as in other studies dealing

with the transformation between colour spaces (Johnson, 1996),

the mean colour differences between measured and estimated val-

ues were lower using the polynomial transformations (Table 2 ), as

concluded from results shown in Table 3.

The prediction from the mobile phone of tristimulus XYZ proved

more accurate than did the Munsell parameters HVC with either

transformation, according to the statistical coefﬁcients listed in Ta-

ble 3. These coefﬁcients are the Pearson's linear correlation coefﬁ-

cient, the goodness of ﬁt (GFC) coefﬁcient (Romero et al., 1997 ), the

root mean square (RMSE) error, and the standardized residual sum

of squares (STRESS) index (García et al., 2007 ), which values for

perfect ﬁt are 1, 1, 0 and 0, respectively. The estimates of Hwere

the worst. This indicates that using a spectroradiometer as a refer-

ence made it possible to ﬁnd better transformation equations, pre-

sumably because the mobile-phone camera and spectroradiometer

worked under the same illumination conditions (D65), including

light quality and intensity, whereas Munsell colour space was de-

ﬁned with illuminant C . On the other hand, the RGB and XYZ colour

spaces have similar geometries, whereas the cylindrical structure

of Munsell system, where H is the angular coordinate, could ham-

per the transformation functions. It is known that when the Mun-

sell radial coordinate C is low the uncertainty in the angular

coordinate H is higher, because H becomes indeterminate at null

chroma C . In our case, for example, of the 35 chips (16 in the chart

5Y and 12 in the chart 10R) estimated with

H> 4 units, 72% had

C62, 20% were extremely light and chromatic (7/8, 8/8), and 8%

had C = 3. Accordingly, our H model performed worst for extreme

colours located in the bottom left corner and upper right corner

of some Munsell charts.

With the polynomial transformation, the average of the colour

differences between measured and predicted values in the 238

chips of Munsell charts was 2.0 ± 1.1 CIELAB units. It is noteworthy

that more than 90% of the samples were measured by the mobile

Table 3

Some statistics of the relationships between colour coordinates measured with the spectroradiometer (XYZ ) or annotated in the Munsell soil-colour charts (HVC) and computed

from the RGB values registered by the HTC smartphone, using the empirical polynomial transformation and ColoSol conversion program (n = 238 chips of the Munsell soil-colour

charts, r = Pearson's correlation coefﬁcient, GFC = goodness of ﬁt coefﬁcient, RMSE = root mean square error, STRESS = standardized residual sum of squares). The mean and

standard deviation of CIELAB (

) and Munsell (

) colour differences are also given.

Polynomial ColoSol

XYZXY Z

r0.9971 0.9972 0.9973 0.9942 0.9945 0.9904

GFC 0.9992 0.9992 0.9990 0.9964 0.9948 0.9892

RMSE 1.4 1.3 1.2 2.9 4,3 5.5

STRESS 4.0 4.1 4.5 8.5 10.2 14.6

Mean

2.0 14.0

1.1 4.5

HVC HV C

r0.8313 0.9977 0.9708 0.8795 0.9965 0.9764

GFC 0.9882 0.9998 0.9919 0.9776 0.9964 0.9924

RMSE 2.75 0.12 0.52 5.4 0.6 2.0

STRESS 15.31 2.07 12.7 21.0 8.5 12.3

Mean

C2.1 0.1 0.4 4.7 0.5 1.8

C1.7 0.1 0.3 2.6 0.3 0.9

Mean

1.1 3.2

0.5 1.1

012345678910

100

Soil samples

Munsell

NCS

Fig. 5. Percentage of samples with a CIELAB colour difference





between

measured (spectroradiometer and estimated (HTC smartphone) values lower than a

ﬁxed X value in 238 Munsell chips, 60 soil samples, and 40 NCS samples.

L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208 205

phone with accuracy of 4 CIELAB units or better (Fig. 5 ). When it is

taken into account that the theoretical colour visual threshold was

around 1.0 CIELAB units, that the practical threshold for most col-

orimetric applications is around 3–4 units, and that only thresh-

olds greater than 5–6 units should be considered erroneous

(Melgosa et al., 1992; Huang et al., 2012 ), the current results sug-

gest that the implementation of polynomial transformations from

RGB to XYZ or HVC in the mobile-phone application is the best op-

tion to provide accurate colour information.

3.2. Performance of the mobile-phone application in the validation

samples

The reliability of the mobile phone to detect colour was initially

checked in the NCS samples covering the colour gamut of the Mun-

sell soil-colour charts. Due partly to the even surface and homoge-

nous colour of the samples, as well as the stability of illumination,

the replicated measurements in each sample had negligible disper-

sion (standard deviation <0.06 in RGB with the mobile phone and

<0.98 in XYZ with the spectroradiometer and <0.21 in HVC with

the spectrophotometer). The differences in each Munsell colour

coordinate (absolute values) between the determinations made

with the app in the HTC mobile phone and the ones provided by

our spectrophotometer are shown in Fig. 6 and the averages are

listed in Table 4 . The hue differences (

H) ranged between 4

and 7, with 70% of the samples within the [ 2, 2] interval. A closest

agreement was found in Munsell value and chroma , with a range of

Vand

Cdifferences [ 2, 2] and [0, 2] units, respectively, most of

the colour samples being within the interval [0, 1]. Despite the low

mean value in

H(Table 4 ), its standard deviation was on the same

order, indicating that some colour samples had a major error.

According to the CIELAB differences found between the mobile

phone estimates and spectroradiometric measurements (CIE tri-

stimulus values XYZ ), the NCS samples with the worst results were,

like the above-mentioned major error in Munsell chips, samples

yellower than 2.5Y (H > 22.5) and with low chroma or very light

and chromatic (samples NCS-25, NCS-22, NCS-9, and NCS-1 in Ta-

ble 5). Although the mean difference was 6.45 CIELAB units, it

reached up to 9.4 CIELAB units in some extreme samples (Table 5),

which produced the high standard deviation value of 2.05 CIELAB

units listed in Table 4 . Around 50% of the NCS samples were mea-

sured using the HTC mobile phone with an error of less than 6 CIE-

LAB units (Fig. 5).

The measuring errors of the mobile phone dramatically de-

creased in soil samples with respect to those in NCS samples when

the spectroradiometer was considered as the reference (Fig. 5 ), and

consequently their mean



was lower in Table 4 , but increased

somewhat when the spectrophotometer was considered (Fig. 6),

primarily

H. On the one hand, this different behaviour could be

attributed both to the illumination conditions, identical only for

the phone camera and spectroradiometer, and the problems once

again with the Munsell space geometry. On the other hand, the

best



results in ﬁne-earth soil samples, despite their uneven

and textured surface with heterogeneous soil-pigment mixtures,

might be explained by the fact that, although covering a wide ga-

mut from 2.9YR to 2.3Y, these Mediterranean soil samples did

not have extreme colours in hue, lightness, or chromaticity, which

must have improved the performance of the mobile phone. Proba-

bly the relatively worse



results for the ground and homoge-

nized soil samples than for ﬁne-earth soil samples (Table 4 ) was

also due to a somewhat more limiting colour gamut in the 15 soils

from which ﬁne earth as well as ground samples were prepared.

For these 15 soils (on the average, 1.0Y and 3.0 in chroma), how-

ever, there was no statistically signiﬁcant difference (P < 0.05) be-

tween the mobile-phone errors in ﬁne earth (mean



¼4:46)

and ground (mean



¼5: 04) samples. The results indicate that

although the ground samples exhibited a more homogeneous as-

pect, it did not lessen the measuring errors of mobile phone, prob-

ably because of the way in which the colour of all pixels in a

picture were managed in order to compute the soil colour as a

whole. The mobile phone calculated the mode of the pixels present

in an area of interest and with the use of this statistical parameter,

anomalous pixels in a picture of ﬁne earth did not inﬂuence the ﬁ-

nal colour coordinates.

Table 5, which lists the best and worst study cases with their

colour coordinates measured with the spectrophotometer (HVC),

spectroradiometer (XYZ ), and mobile phone (RGB ), conﬁrmed the

good results for the soil samples: SOIL-va, SOIL-ck, SOIL-sn, SOIL-

012345678910

100

ΔH

NCS

Soils

012345678910

100

ΔV

NCS

Soils

012345678910

100

ΔC

NCS

Soils

Fig. 6. Histograms for NCS (n = 40) and soil (n= 60) samples with the differences

(absolute value) in Munsell hue (

H), value (

V), and chroma (

C) between data

measured with the spectrophotometer and predicted with the HTC smartphone.

206 L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208

ll, and SOIL-sh even with errors close to the theoretical colour

threshold (1–2 CIELAB units). There was, however, an apparent

lack of consistency in the magnitudes of CIELAB





and Munsell

(

C) differences between measured and predicted values.

Thus, for example, SOIL-sh had greater CIELAB difference (1.9

units) than SOIL-va (1.3 units), just the opposite behaviour that

Munsell differences (0.3, 0.2, 0.1 against 0.6, 0.1, 0.2, for

Crespectively). This fact, common in Table 5 , has already been

statistically-discussed with the results of Table 4 and Figs. 5 and

6, indicating that there was no a good correlation between spectro-

photometric and spectroradiometric measurements. In this way,

higher values in



did not necessarily correspond to higher

errors in HVC and vice versa.

Considering the entire dataset of soil samples (n = 60), we found

that the discrepancies between the HTC smartphone and spectro-

photometer ranged from  6 to 6 for Munsell hue , so that 60% of

samples were determined with an error of less than 3.0 units

(Fig. 6 ). For Munsell value and chroma , most soil samples had an er-

ror of less than 1.0 Munsell unit. The total colour difference be-

tween HTC smartphone and spectroradiometer was on the

average 3.7 ± 1.8 CIELAB units, and more than 80% of soil samples

were measured by the mobile phone with an error lower than 5

CIELAB units (Fig. 5 ). It also bears mentioning that this error by

the mobile phone in sensing colour is lower than those previously

reported when the same soil samples were visually determined

using Munsell soil-colour charts. Speciﬁcally, a group of 10 soil sci-

entists judging soil colour under controlled illumination had a

mean error of 4.4 CIELAB units in ground soil samples and 10.2

in aggregated soil samples with respect to instrumental measure-

ment, and their inter-observer variability, i.e. disagreement among

the ten Munsell notations on a given sample, was 5.1 CIELAB units

(Sánchez-Marañón et al., 1995 ). Differences of up 5.5 CIELAB units

were also measured by Sánchez-Marañón et al. (2005) in Munsell

soil-colour charts from different editions, manufacturers, and de-

gree of use, so inducing the same error in the soil colour determi-

nation. In addition, the error made by the smartphone estimating

Munsell hue (59.0% of soils with

H> 2 units, Fig. 6 ) was lower

than the variation in Munsell hue of soils observed under different

natural daylight conditions (78.6% of soils with

H> 2 units, Sán-

chez-Marañón et al., 2011).

Finally, once the mobile-phone application developed with the

HTC smartphone was installed in another device (Samsung smart-

Table 4

Differences in Munsell hue , value , and chroma (

V, and

Cin absolute value) and CIELAB colour differences D E



between data measured with a spectrophotometer (HVC)

and a spectroradiometer (XYZ ) and estimated with two mobile phones (HTC and Samsung).

Group of samples Colour parameter Mean value Standard deviation

NCS samples

HTC smartphone (n = 40)

H1.69 1.69

V0.66 0.22

C0.36 0.34



6.45 2.05

Ground soil samples

HTC smartphone (n = 15)

H2.05 1.23

V0.57 0.31

C0.32 0.20



5.04 2.47

Fine-earth soil samples

HTC smartphone (n = 45)

H2.72 1.70

V0.57 0.36

C0.81 0.60



3.31 1.59

Soil samples (ﬁne-earth plus ground samples)

Samsung smartphone (n = 60)

H3.38 1.55

V0.63 0.26

C0.25 0.17



5.46 2.46

Table 5

The ﬁve NCS- and SOIL-samples with the lowest (italics) and highest CIELAB colour differences DE 



between the colour measured with a spectroradiometer (XYZ , mean of 3

replicates with SD < 1.38) and predicted from the RGB values (mean of 3 replicates with SD < 0.11) by the HTC smartphone. The Munsell HVC measured with a spectrophotometer

(mean of 3 replicates with SD < 0.24) and the differences (

C) with the predicted values by the mobile phone are also listed.

Sample HVCX YZ R G B



NCS-38 16.9 7.7 0.5 55.3 57.6 63.5 222.0 202.0 206.0 4.6 0.4 0.3 2.3

NCS-28 12.9 7.5 1.1 57.2 58.9 62.2 231.0 202.0 198.0 1.9 0.7 0.3 2.6

NCS-21 10.4 3.7 6.6 14.8 11.3 5.5 165.0 41.0 41.0 1.1 0.4 0.1 2.7

NCS-30 13.7 6.5 7.0 48.3 43.1 22.7 255.0 145.0 107.0 2.3 0.9 0.6 3.9

NCS-35 15.1 6.3 8.0 44.7 39.8 16.1 255.0 136.0 74.0 3.6 0.9 1.3 4.2

SOIL-va 21.7 4.4 1.9 12.7 13.0 9.5 105.6 75.2 56.3 0.6 0.1 0.2 1.3

SOIL-ck 21.4 6.0 2.7 30.7 31.6 21.9 166.4 135.6 94.2 3.5 0.0 0.1 1.7

SOIL-sn 19.9 4.9 3.6 21.9 21.2 12.7 149.4 98.0 56.3 2.3 0.2 0.0 1.8

SOIL-ll 19.9 4.0 2.6 20.6 20.9 13.3 140.8 98.0 65.5 2.1 1.0 0.3 1.8

SOIL-sh 19.5 3.8 2.6 11.9 11.2 7.3 105.6 59.1 39.9 0.3 0.2 0.1 1.9

NCS-4 18.2 6.2 5.0 35.2 34.2 18.2 231.0 156.0 99.0 0.3 1.0 0.6 9.2

NCS-25 23.1 3.3 2.7 6.7 6.9 3.8 99.0 65.0 41.0 1.6 0.8 0.3 9.4

NCS-22 23.3 7.6 2.1 41.3 43.6 34.4 222.0 193.0 156.0 1.3 0.2 0.2 9.4

NCS-9 25.5 4.3 2.5 14.7 15.7 10.2 140.0 112.0 74.0 0.4 1.0 0.0 9.4

NCS-1 22.5 7.1 6.3 40.0 40.9 16.1 239.0 179.0 82.0 0.1 0.6 0.0 9.4

SOIL-vg 22.6 5.1 2.3 23.4 24.3 19.2 157.9 124.8 85.0 1.8 0.6 0.4 7.6

SOIL-dd 19.8 5.1 3.7 26.9 27.0 15.1 181.0 110.4 74.0 1.6 0.6 0.8 7.8

SOIL-ps 15.9 4.0 5.0 20.2 18.3 6.8 167.7 65.4 33.0 0.5 0.7 1.3 7.9

SOIL-mo 22.2 5.4 2.1 23.5 24.6 17.1 178.3 129.1 109.7 4.2 0.5 0.6 9.8

SOIL-bc 20.5 6.1 3.9 32.8 33.2 19.6 221.3 162.4 103.4 1.2 1.1 0.8 11.1

L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208 207

phone), we measured the soil samples again under the same exper-

imental conditions (Fig. 2 ). The results with the Samsung smart-

phone were only slightly worse than using the HTC (Table 4 ) but

still acceptable. This indicates similarities in the performance of

both mobile-phone cameras to capture the RGB signals and facili-

ties in the operating system to interchange the software.

4. Conclusions

Our results indicate that the technical resources of the current

mobile phones can be exploited to use these electronic devices,

which are prevalent worldwide and accessible to everyone, as

soil-colour sensors. The RGB signals captured by the camera and

converted in colour coordinates by a software application running

inside the same smartphone, can thus achieve objective, easy, ra-

pid, and cheap colour measurements. Under controlled illumina-

tion conditions, the measurements seem to be also more

accurate than visual soil-colour determination using Munsell

charts.

Essential requirements of the mobile-phone application were a

moveable and visible frame on the smartphone screen to select the

region of interest in the picture, a calibration or normalization tool

for referencing colour to a standard white, and transformation

equations to convert RGB values in Munsell or CIE colour

coordinates.

In converting the image signals to colour, a polynomic-pro-

cess transform ﬁtted to the colour gamut of the Munsell soil-col-

our charts worked better than standard equations from the

colour literature, and also proved more efﬁcient in reference to

XYZ coordinates (very similar in geometry to RBG) measured

with a spectroradiometer under the same experimental condi-

tions as the phone camera. Accordingly, although our initial

objective was to reproduce the Munsell notation of soil samples,

the results suggest that CIE coordinates are more accurately pre-

dicted for this application.

Programming in Android allowed the application to be success-

fully interchanged between different devices. However, to achieve

an application working similarly in different models of mobile

phones constitutes a challenge, because it depends mainly on the

quality of mobile-phone camera. The use of the mobile phone un-

der non-controlled (variable) illumination conditions is another

step forward for our future research.

Acknowledgements

This work has been partially funded by Ministry of Economy

and Competitivity (Spain) under FIS2010-19839 Research Project,

and Junta de Andalucía (Spain) under Project PE10-TIC5997, with

European Regional Development Fund (ERDF) support.

References

ASTM, 2008. Standard Practice for Specifying Color by the Munsell System. ASTM

International D 1535-08, PA, USA.

Aydemir, S., Keskin, S., Drees, L.R., 2004. Quantiﬁcation of soil features using digital

image processing (DIP) techniques. Geoderma 119, 1–8.

Berns, R., 2000. Billmeyer and Saltzman's Principles of Color Technology. Wiley

Interscience.

Cadle, B.A., Rasmus, K.C., Varela, J.A., Leverich, L.S., O'Neill, C.E., Bachtell, R.K.,

Cooper, D.C., 2010. Cellular phone-based image acquisition and quantitative

ratiometric method for detecting cocaine and benzoylecgonine for biological

and forensic applications. Subst. Abuse 4, 21–33.

CIE, 2004. Publication 15:2004. In: Bureau, C.C. (Ed.), Colorimetry, 3rd ed., Vienna.

García, P.A., Huertas, r., Melgosa, M., Cui, G., 2007. Measurement of the relationship

between perceived and computed color difference. J. Opt. Soc. Am. A 24, 1823–

1829.

García, A., Erenas, M.M., Marinetto, E.D., Abad, C.A., de Orbe-Paya, I., Palma, A.J.,

Capitán-Vallvey, L.F., 2011. Mobile phone platform as portable chemical

analyzer. Sensor. Actuat. B – Chem. 156, 350–359.

Godlove, I.H., 1951. Improved color-difference formula, with applications to the

perceptibility and acceptability of fadings. J. Opt. Soc. Am. 41, 760–772.

Hong, G., Luo, M.R., Rhodes, P.A., 2001. A study of digital camera colorimetric

characterization based on polynomial modeling. Color Res. Appl. 26, 76–84.

Huang, M., Liu, H., Cui, G., Luo, M.R., Melgosa, M., 2012. Evaluation of threshold color

differences using printed samples. J. Opt. Soc. Am. A 29, 883–891.

IUSS Working Group WRB, 2006. World reference base for soil resources 2006.

World Soil Resources Reports 103.

Johnson, T., 1996. Methods for characterizing colour scanners and digital cameras.

Displays 16, 183–191.

León, K., Mery, D., Pedreschi, F., León, J., 2006. Color measurement in L

⁄

units

from RGB digital images. Food Res. Int. 39, 1084–1091.

Martinez, A.W., Phillips, S.T., Carrilho, E., Thomas 3rd., S.W., Sindi, H., Whitesides,

G.M., 2008. Simple telemedicine for developing regions: camera phones and

paper-based microﬂuidic devices for real-time, off-site diagnosis. Anal. Chem.

80, 3699–3707.

Melgosa, M., Hita, E., Romero, J., del Barco, L.J., 1992. Some classical color differences

calculated with new formulas. J. Opt. Soc. Am. A 9, 1247–1254.

Meyer, G.E., Camargo Neto, J., Jones, D.D., Hindman, T.W., 2004. Intensiﬁed fuzzy

clusters for classifying plant, soil, and residue regions of interest from color

images. Comput. Electron. Agric. 42, 161–180.

Munsell Color Company, 2000. Munsell Soil Color Charts. Munsell Color Co.,

Baltimore, MD.

Nakamoto, T., Ikeda, T., Hirano, H., Arimoto, T., 2009. Humidity compensation by

neural network for bad-smell sensing system using gas detector tube and built-

in camera. 2009 IEEE Sensors, pp. 281–286.

NCS – Natural Colour System

Ó, 2013. <http://www.ncscolour.com/en/ncs/>.

O'Donnell, T.K., Goyne, K.W., Miles, R.J., Baffaut, C., Anderson, S.H., Sudduth, K.A.,

2011. Determination of representative elementary areas for soil redoximorphic

features identiﬁed by digital image processing. Geoderma 161, 138–146.

Penrose, R., 1955. A generalized inverse for matrices. In: Mathematical Proceedings

of the Cambridge Philosophical Society, p. 51.

Puder, A., Antebi, O., 2013. Cross-compiling Android applications to iOS and

Windows Phone 7. Mobile Netw. Appl. 18, 3–21.

Rodriguez-Pulido, F.J., Gomez-Robledo, L., Melgosa, M., Gordillo, B., Gonzalez-Miret,

M.L., Heredia, F.J., 2012. Ripeness estimation of grape berries and seeds by

image analysis. Comput. Electron. Agric. 82, 128–133.

Romero, J., García-Beltran, A., Hernández-Andrés, J., 1997. Linear bases for

representation of natural and artiﬁcial illuminants. J. Opt. Soc. Am. A 15,

1007–1014.

Salmeron, J.F., Gomez-Robledo, L., Carvajal, M.A., Huertas, R., Moyano, M.J., Gordillo,

B., Palma, A.J., Heredia, F.J., Melgosa, M., 2012. Measuring the colour of virgin

olive oils in a new colour scale using a low-cost portable electronic device. J.

Food Eng. 111, 247–254.

Sánchez-Marañón, M., Delgado, G., Delgado, R., Pérez, M.M., Melgosa, M., 1995.

Spectroradiometric and visual color measurements of disturbed and

undisturbed soil samples. Soil Sci. 160, 291–303.

Sánchez-Marañón, M., Soriano, M., Melgosa, M., Delgado, G., Delgado, R., 2004.

Quantifying the effects of aggregation, particle size and components on the

colour of Mediterranean soils. Eur. J. Soil Sci. 55, 551–565.

Sánchez-Marañón, M., Huertas, R., Melgosa, M., 2005. Colour variation in standard

soil-colour charts. Aust. J. Soil Res. 43, 827–837.

Sánchez-Marañón, M., García, P.A., Huertas, R., Hernandez-Andres, J., Melgosa, M.,

2011. Inﬂuence of natural daylight on soil color description: assessment using a

color-appearance model. Soil Sci. Soc. Am. J. 75, 984–993.

Soil Survey Staff, 1993. Soil survey manual. USDA Handbook.

Soil Survey Staff, 1999. Soil Taxonomy: A Basic System of Soil Classiﬁcation for

Making and Interpreting Soil Surveys, 2nd ed.

Torrent, J., Barrón, V., 1993. Laboratory measurement of soil color: theory and

practice. In: Bigham, J.M., Ciolkosz, E.J. (Eds.), Soil Color, Special Publication No.

31, SSSA, Madison, WI, pp. 21–33.

Viscarra Rossel, R.A., 2006. ColoSol. A Colour Conversion Program for Soil Colour

v3.0. <http://www.usyd.edu.au/su/agric/acpa/people/rvrossel/>.

Viscarra Rossel, R.A., Webster, R., 2011. Discrimination of Australian soil horizons

and classes from their visible-near infrared spectra. Eur. J. Soil Sci. 62, 637–647.

Viscarra Rossel, R.A., Minasny, B., Roudier, P., McBratney, A.B., 2006. Colour space

models for soil science. Geoderma 133, 320–337.

Viscarra Rossel, R.A., Fouad, Y., Walter, C., 2008. Using a digital camera to measure

soil organic carbon and iron contents. Biosyst. Eng. 100, 149–159.

Viscarra Rossel, R.A., Adamchuck, V.I., Sudduth, K.A., McKenzie, N.J., Lobsey, C.,

2011. Proximal soil sensing: an effective approach for soil measurements in

space and time. Adv. Agron. 113, 243–291 (Chapter 5).

Westland, S., Ripamonti, C., 2004. Computational Colour Science using Matlab, 1st

ed. Wiley, New York.

Wyszecki, G., Stiles, W.S., 2000. Color Science: Concepts and Methods, Quantitative

Data, and Formulae. John Wiley & Sons, New York.

208 L. Gómez-Robledo et al. / Computers and Electronics in Agriculture 99 (2013) 200–208

... The main challenge with the Munsell colour scheme is that several problems have been routinely mentioned in the literature with the consistency and accuracy in colour determination [20,[26][27][28][29][30]. This is because the perception of colour attributes is affected by numerous psychophysical factors, such as environmental conditions (e.g., moisture content, illumination conditions) [31], sample characteristics (e.g., size, roughness), difficult statistical analysis (e.g., limited colour chips, cylindrical colour coordinates) [32,33], and the observer's sensitivities (e.g., colour blindness, subjectivity, poor colour memory, eye fatigue) [20,29,[31][32][33][34][35]. ...

... This is consistent with other studies that have found a lower percentage agreement for these colour attributes because observers making these measurements show a preference for extreme numbers to differentiate amongst similar colours [26,30]. This work brings up the potential biases in observer colour perception, and the dilemma of uncertainty in colour determination using MSCC that is routinely mentioned in the literature [20,30,33,34]. ...

... This work brings up one of the primary drawbacks of using the MSCC for any observer: the variation in individual perception of soil colour [20,29,[31][32][33][34][35]. However, aside from the observer's sensitivities, there are numerous other psychophysical and physical factors that users have identified as potential sources of discrepancy in the results [37], including (1) sample characteristics (e.g., size, roughness), (2) environmental conditions (e.g., moisture content, lighting conditions) [31], and (3) difficult statistical analysis (e.g., limited colour chips, cylindrical colour coordinates) [32,33]. ...

Rapid, low-cost methods for large-scale assessments of soil organic carbon (SOC) are essential for climate change mitigation. Our work explores the potential for citizen scientists to gather soil colour data as a cost-effective proxy of SOC instead of conventional lab analyses. The research took place during a 2-year period using topsoil data gathered by citizen scientists and scientists from urban parks in the UK and France. We evaluated the accuracy and consistency of colour identification by comparing "observed" Munsell soil colour estimates to "measured" colour derived from reflectance spectroscopy, and calibrated colour observations to ensure data robustness. Statistical relationships between carbon content obtained by loss on ignition (LOI) and (i) observed and (ii) measured soil colour were derived for SOC prediction using three colour components: hue, lightness, and chroma. Results demonstrate that although the spectrophotometer offers higher precision , there was a correlation between observed and measured colour for both scientists (R 2 = 0.42; R 2 = 0.26) and citizen scientists (R 2 = 0.39; R 2 = 0.19) for lightness and chroma, respectively. Foremost, a slightly stronger relationship was found for predicted SOC using the spectrophotometer (R 2 = 0.69), and citizen scientists produced comparable results (R 2 = 0.58), highlighting the potential of a large-scale citizen-based approach for SOC monitoring.

... It has been shown that the effect of SOM on the visible wavelengths can be reflected in the photo color, thus allowing the development of prediction studies based on the correlation between the photo color and SOM content (Heil et al., 2020). Gómez-Robledo et al. (2013) indicated that smart phones can be used as Munsell soil-color sensors, and the measurement results were more accurate than visual soil-color determination. A study showed that the RGB values from digital images were highly relevant to measurements performed by a field spectrometer, and the camera can be used as an available analysis tool for the analysis of soil color (Levin et al., 2005). ...

... Different hardware configurations and software parameters of equipment may cause differences in the colors of photos (Hong et al., 2001). Gómez-Robledo et al. (2013) proposed that due to differences in the quality of the smart phone camera, the reliability of the results was different when running the same program to predict soil parameters on different models of smart phones. The researchers noted that image color information needs to be calibrated using a standard reference in the face of different light conditions, which reinforced the ability of the phone device to be used under varying conditions (Fan et al., 2017;Fu et al., 2020;Gómez-Robledo et al., 2013). ...

... Gómez-Robledo et al. (2013) proposed that due to differences in the quality of the smart phone camera, the reliability of the results was different when running the same program to predict soil parameters on different models of smart phones. The researchers noted that image color information needs to be calibrated using a standard reference in the face of different light conditions, which reinforced the ability of the phone device to be used under varying conditions (Fan et al., 2017;Fu et al., 2020;Gómez-Robledo et al., 2013). Other researchers found that the color prediction results from both cameras could not be effectively calibrated by using an external standard (Kirillova et al., 2021). ...

Jiawei Yang
Feilong Shen
Tianwei Wang
Shuxin Que

Compared with the complicated operation of traditional laboratory methods or expensive spectral instruments, soil organic matter (SOM) content prediction based on smart phone photos has recently received heightened attention. However, as one of the most popular mobile devices, the imaging characteristics of smart phone cameras are quite different due to the differences in manufacturer technologies, which may affect the relationship between the photo colors and SOM content. Whether the highly accurate model built based on a single phone can be applied to other phone types is still an open question. This study has validated the shared capacity of color-based prediction models, analyzed the intrinsic factors affecting the shared capacity, and proposed potential methods to enhance the shared capacity. In total, five smart phones were selected for the study, and dried soil samples were photographed in an optical dark chamber. Imaging spectroscopy was used to scan the samples. The photo and spectral data were pretreated, and stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) models were built. The spectral response curves of the five smart phone cameras were also obtained separately to clarify their imaging characteristics. Results indicated the RGB color distribution conditions of soil photos obtained by different phones were different, which affects the correlation between the color parameters and SOM. The prediction ability of the models constructed by the five smart phones were similar to the spectral devices, achieving an R² of 0.68–0.77 and an RMSE of 5.32–7.12 g/kg. However, when substituting the color parameter datasets obtained by the five phones into the models constructed by the other phones to verify the shared capacity, we found that most of the prediction results could not meet the requirements for use. The poor shared capacity might be extremely disruptive to users. We proposed several potential methods that might enhance the model's shared capacity. The results of this study showed that smart phone cameras have a good capability of modeling SOM content independently, but the shared capacity of different phones still needs further investigation.

... Even though the DRS represents a single investment in equipment, its use in the field is limited and expensive, and not everyone can have easy access to this technology (Han et al., 2016). New initiatives have suggested the use of mobile proximal sensors (MPS) as a low-cost alternative to characterize color and other soil attributes (Aitkenhead et al., 2018;Gómez-Robledo et al., 2013;Han et al., 2016;Levin et al., 2005;Simon et al., 2020;Viscarra Rossel, Minasny, et al., 2006;. The MPS technique provides images in pixels, which can be converted into standard RGB (red-green-blue) colors from their computational processing (Aitkenhead et al., 2018;Gómez-Robledo et al., 2013;Morais et al., 2020;Viscarra Rossel, Walvoort, et al., 2006). ...

... New initiatives have suggested the use of mobile proximal sensors (MPS) as a low-cost alternative to characterize color and other soil attributes (Aitkenhead et al., 2018;Gómez-Robledo et al., 2013;Han et al., 2016;Levin et al., 2005;Simon et al., 2020;Viscarra Rossel, Minasny, et al., 2006;. The MPS technique provides images in pixels, which can be converted into standard RGB (red-green-blue) colors from their computational processing (Aitkenhead et al., 2018;Gómez-Robledo et al., 2013;Morais et al., 2020;Viscarra Rossel, Walvoort, et al., 2006). The RGB color system allows digital images to be transformed into standard color space (Gómez-Robledo et al., 2013). ...

... The MPS technique provides images in pixels, which can be converted into standard RGB (red-green-blue) colors from their computational processing (Aitkenhead et al., 2018;Gómez-Robledo et al., 2013;Morais et al., 2020;Viscarra Rossel, Walvoort, et al., 2006). The RGB color system allows digital images to be transformed into standard color space (Gómez-Robledo et al., 2013). The advantage is the use of a portable tool, such as a mobile phone, with the potential for a proximal field sensor (Aitkenhead et al., 2018;Fan et al., 2017;Han et al., 2016). ...

Detailed mapping is essential for land use and management planning. The mappings require a robust database. Costs and time associated with obtaining the database are high and, therefore, it is not always possbile to obtain it. Soil color is a pedoindicator attribute that can be easily characterized. In this sense, this study aimed to use soil color, based on the RGB system and obtained by diffuse reflectance spectroscopy (DRS) and mobile proximal sensor (MPS), to estimate mineralogical attributes using machine learning techniques for the Western Plateau of São Paulo. A total of 600 samples were collected throughout the study area. The samples were analyzed by DRS and then photographed. The color data were obtained by the RGB system after analysis in a computer program. The samples were subjected to laboratory analysis to quantify the contents of crystalline and non‐crystalline iron, hematite, goethite, kaolinite, and gibbsite. The database was subjected to the random forest machine learning algorithm and geostatistics. The use of random forest allowed estimating soil mineralogical attributes based on the RGB system by DRS and MPS. Detailed maps of mineralogical attributes could be constructed using the RGB system by the DRS and MPS techniques. The MPS technique can be used to characterize soil color, reducing the costs associated with analysis and the time required for data collection. This article is protected by copyright. All rights reserved

... Smartphones are ubiquitous, cheaper than traditional spectrometers, lightweight, field-portable, and less subjective in detecting soil colour than the MSCC. Gomez-Robledo et al. (2013) reported the ability of a smartphone as a reliable soilcolour sensor. Further, Moonrungsee, Pencharee, and Jakmunee (2015) used a field-deployable colourimetric analyzer based on an Android mobile phone for the determination of available P content in the soil while the results agreed well with the spectrophotometric method, with a detection limit of 0.01 mgPL À1 . ...

... These soils are dark in colour, mainly due to high SOM content (Banerjee et al. 2016(Banerjee et al. , 2018. Conversely, red and laterite zone soils are old, ferruginous, reddish-brown in appearance because of Fe/Al oxides, and have a characteristic honeycomb structure (Ghosh & Guchhait, 2015). These soils are formed from the deposition of coarse ferruginous sediments brought by peninsular rivers like the Damodar, Ajay, Subarnarekha, etc (Niyogi, Mallick, & Sarkar, 1970). ...

This study evaluated a novel smartphone-based soil image segmentation technique and subsequent machine learning (ML) optimization methodology with a set of soil images for rapidly predicting soil organic matter (SOM) with minimal soil processing. A smartphone and a custom-made box were used to capture images for 90 soil samples, collected from three different agroclimatic zones of West Bengal, India under three different illumination conditions. To offset the impact of variable illumination, the reflectance component of the image was recovered by removing the illumination from the image. Further, to deceive the ML model without distorting the soil image, an adversarial image was generated by adding Gaussian noise to the image. A Tree-based Pipeline Optimisation Tool was used to find an optimum ML stacking scheme using six different ML models. Model validation statistics indicated that reflectance image-extracted sub-colour space could predict SOM with reasonable accuracy (R² = 0.88, RMSE = 0.28%) using original images in stack one. Moreover, the sub-colour space using perturbed images in stack one could sense noise, worsening the model validation (R² = 0.79, RMSE = 0.36%). Conversely, seven out of eight tested colour spaces in stack two were unable to sense the image noise, producing higher validation performance than the original images. The proposed smartphone-based image acquisition setup combined with the computer vision and ML pipeline produced an important advance in affordable optical tool-based SOM prediction with significant time and cost savings. More research is warranted to extend this approach by incorporating field images of variable soil types taken under variable illuminations.

... Recently, developed agriculture-related APPs based on android or IOS operating system have been widely used in agricultural production [41], which makes it possible to use the smartphone for collecting varieties of data. For instance, in [42], authors designed an application to analyze brightness with cameras of smartphones, and in [43], mobile phones were used as soil colour sensors. Easy operation and practicability will improve the utilization rate of these APPs, which can generate massive data. ...

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

... Smartphone applications are convenient, low cost, and rapid tools to use in the field. As an alternative soil data capture tool, many studies indicated that the smartphone can be used to estimate several soil morphology features under field and laboratory conditions (Fu et al., 2019;Gómez-Robledo et al., 2013;Han et al., 2016;Stiglitz et al., 2017). SoilWeb also released a smartphone application to expand digital soil survey applications (O'Geen et al., 2017). ...

Zhuo-Dong Jiang
Phillip R. Owens
Chun-Liang Zhang
Qiu-Bing Wang

Soil horizons are the manifestation of pedogenesis and contain the basic morphologic indicators of soil formation. Accurate and quantitative tools for delineating soil horizons in-situ can assist soil scientists towards rapid and dynamic soil survey information. The objective of this study was to develop a deep-learning-based, soil-profile-imaging method for identifying and delineating soil master horizons to assist in digital soil descriptions. A total of 160 soil profile images from four soil orders (Alfisols, Entisols, Inceptisols, and Mollisols) were collected from north China in the Inner Mongolia and Liaoning regions. The 160 profile images were amplified to 2400 individual images for model building using data-augmentation procedures. The augmented profile image dataset was divided into training (70%), validation (15%), and test (15%) datasets. The training and validation datasets were imported into a nested, U-net network for model building. The proposed deep-learning (DL) model classified soil profiles into A, B, and C horizons. The mean pixel accuracy of the DL model was 0.86 with the training dataset, 0.82 with the validation dataset, and 0.83 for the test dataset. Results showed that the DL model was judged to be accurate enough for identifying and delineating soil master horizons from profile images in practice. Based on the proposed DL model, a smartphone application was subsequently developed to digitalize the soil profile and assist field evaluation of soil profile horizonation. The smartphone application, for the Android operating system (version 5.0 and greater), featured a response time of < 5 s. This study demonstrated that the proposed DL model and smartphone application could be a simple, fast, and digital tool for quickly identifying and delineating in-situ soil horizons. These tools allow for rapid data collection, which can be used for future artificial intelligence development and application in soil science towards digital soil profile description and dynamic soil survey.

... Estimation of the soil properties relies on the R-G-B color information of the image pixels. Applying these principles, digital images from cell phones have been used to estimate soil color by Gómez-Robledo et al. (2013) and by Stiglitz et al. (2016) with an additional sensor. Digital images of 8 megapixels are stored on the phone with the JPEG compression format and computing is made by the phone processor. ...

Digital convergence is helping us to better understand and study the soil. Fixed and mobile sensors, and wireless communication systems aided by the internet produce cheap and abundant streams of digital soil data which can readily be used for modeling and information generation. Here we explore the ways in which digital science and technology have affected soil science. We can call this digital soil science and define it as the study of the soil aided by the tools of the digital convergence. To some degree all of our research and teaching had been enabled, enhanced and expanded by the digital convergence. We outline how soil science has changed using illustrations of intellectual and technical developments enabled digitally. Digital soil sensors have been widely implemented, and new tools such as cell phones and apps, or metagenomics techniques are becoming available. There are also areas in soil science for which no major obstacles in the digital technologies exist, but which have not been thoroughly investigated, e.g. to devise a truly digital soil field description or for building a formal digital quantitative system of soil classification. The soil science community will need to be alert to some of the dangers brought by the digital convergence such as the lack of new theory and proprietary (black-box) soil prediction. Finally, we discuss a whole set of digital tools that will, or might, gain the stage in the immediate future, and take a stab in the dark on what may lie over the horizon of digital soil science.

A rapid and cost-effective soil organic carbon (SOC) monitoring is vital for soil management. While SOC laboratory analysis is expensive and time-consuming, spectroscopic sensor technology allows for much faster and inexpensive acquisition of data. Still, the initial cost of spectroscopic sensors is high for farmers. With soil color as a proxy, digital cameras could be used as SOC sensors. We compared the performance of SOC prediction based on soil color on a regional scale. Soil color measurements were made on samples covering the German state of North Rhine-Westphalia. SOC ranged between 0.03 and 4.74%. Images of the samples were taken under standardized conditions with four different sensors. Various soil color space models and indices were derived for SOC prediction. Modeling was performed using multiple linear regression (MLR) and random forest (RF). Best SOC prediction results were achieved using color of wet soils. Best MLR and RF models gave similar validation R² values for the prediction of SOC at 0.66 and 0.63 with RMSE values of 0.57 and 0.61%, respectively. The MLR models using one color space showed the same performance as the RF models using the full vis spectrum. This suggested that the vis spectrum does not contain more information than the three variables of a common color space. A smartphone had the same accuracy as a more professional camera, making it a potential soil sensor. With increasing scale, soil mineralogy influences soil color weakening the performance.

Stephanie A. Schmidt
Changwoo Ahn

Forested wetland soils within the Piedmont and Coastal Plain physiographic provinces of Northern Virginia (NOVA) were investigated to determine the utility of a handheld colorimeter, the Nix Pro Color Sensor ("Nix"), for predicting carbon contents (TC) and stocks (TC stocks) from on-site color measurements. Both the color variables recorded with each Nix scan ("Nix color variables"; n = 15) and carbon contents significantly differed between sites, with redder soils (higher a and h) at Piedmont sites, and higher TC at sites with darker soils (lower values of L, or lightness; p < 0.05). Nix–carbon correlation analysis revealed strong relationships between L (lightness), X (a virtual spectral variable), R (additive red), and KK (black) and log-transformed TC (Ln[TC]; |r| = 0.70; p < 0.01 for all). Simple linear regressions were conducted to identify how well these four final Nix variables could predict soil carbon. Using all color measurements, about 50% of Ln(TC) variability could be explained by L, X, R, or KK (p < 0.01), yet with higher predictive power obtained for Coastal Plain soils (0.55 < R² < 0.65; p < 0.01). Regression model strength was maximized between Ln(TC) and the four final Nix variables using simple linear regressions when color measurements observed at a specific depth were first averaged (0.66 < R² < 0.70; p < 0.01). While further study is warranted to investigate Nix applicability within various soil settings, these results demonstrate potential for the Nix and its soil color measurements to assist with rapid field-based assessments of soil carbon in forested wetlands.

Soil organic matter (SOM) plays a key role in ecosystems. Reduction of its content due to land-use changes has a negative impact on the soil, but also on the wider environment. Accordingly, SOM content is routinely analyzed in the laboratory. As these are expensive and/or time-consuming, indirect ones are also tested. The aim of this study was to examine the possibility of predicting SOM content by linear regression using soil color as the predictor, at three locations in Zagreb (Croatia), with different soil types (eutric cambisol anthropogenic, humofluvisol, pseudogley) and different land uses (plough land, meadow, forest, respectively). At each location, 5 samples of the surface soil layer were taken. Soil color was determined using the Munsell system, and the hue was 2.5Y and 10YR in dry and moist soil, respectively. Laboratory analyzes showed that the soils are very acid to neutral silt loams. In line with the land-use, they differed significantly in SOM content and were poorly humic (plough land), moderately to highly humic (meadow), and highly humic (forest). Correlation between soil color dimensions and SOM content was significant only for the dry samples, between chroma and SOM and between value/chroma ratio and SOM. Regression analysis showed high coefficients of determination for these two relationships (R2 = 0.88 for chroma-SOM, R2 = 0.76 for value/chroma-SOM). The results suggest that visual soil color determination can be used to estimate SOM content, but only in dry soil. The model calibrated in this paper needs to be validated using samples of other (different) soils.

Determination of soil color is useful to characterize and differentiate soils. The color of soil materials can be measured in the laboratory by using diffuse reflectance spectrophotometers. The spectral reflectance data given by these apparatuses are easily converted to three figures (`tristimulus values') that define the color perceived by the human eye. In turn, tristimulus values can be converted to the Munsell notation or the parameters of other color systems. Modern, commercially available spectrophotometers not only allow a quick measurement of reflectance but usually provide color data in different systems. If care is taken in obtaining homogeneously granulated or powdered soil samples, and in preparing the white reflectance standards, high accuracy and precision are obtained. Small differences in soil color can then be used to identify and study differences in soil compositional properties. For this purpose, several `color indices' calculated from the color data can also be used.

Soil color is usually described under natural daylight using Munsell charts. Daylight can vary but its effects on the Munsell notation are hardly known. Today, color-appearance models allow quantitative analyses of the perceived color under different lights. Using the CIECAM02 model, we studied the color changes in 238 Munsell chips and 229 soils under 125 types of daylight as well as their effects when matching soil to chips. The different types of daylight were measured from spectral power distributions and color attributes from reflectance spectra. Relationships (r = -0.94 to 0.95) between master variables taken from categorical principal-component analyses showed that as daylight becomes bluer (3758-34,573 K), samples with a concave spectrum between 500 and 600 nm redden while those of a convex spectrum turn yellow. This hue angle change (0.5-29.5 degrees), especially great in slightly chromatic samples (r(2) = 0.90), was different enough in the soil and chip of most color matches to generate paramerism. The practical implication is that 79% of the soils had more than one Munsell notation because of daylight changes, although this decreased to 19% considering only daylight at a solar elevation of >9 degrees. This finding supports the good practice of pedologists in determining soil color in the central hours of the day; however, 45% of soils with reflectance spectra close to several chips had Munsell colors either redder or yellower under midday light (5933 + 481 K) than under the C reference illuminant of the Munsell system (6800 K). Unless the reflectance spectrum of the sample is available, it is not possible to know if soil colors have been correctly denoted or not, or how to compensate for the differences.

The growing importance of colour science in manufacturing industry has resulted in the availability of many excellent text books: existing texts describe the history and development of the CIE system, the prediction of colour difference and colour appearance, the relationship of the CIE system to the human visual system, and applications of colour science in technology. However, the field of colour science is becoming ever more technical and although practitioners need to understand the theory and practice of colour science they also need guidance on how to actually compute the various metrics, indices and coordinates that are useful to the practicing colour scientist. Computational Colour Science Using MATLAB was published to address this specific need. It described methods and algorithms for actually computing colorimetric parameters and for carrying out applications such as device characterisation, transformations between colour spaces, and computation of various indices such as colour differences. There are a number of reasons why a second edition has now been published. Firstly, the last decade has seen a number of developments that are important but which were not included in the first edition; secondly, some notable topics were omitted from the first edition and are now included as additional chapters in this edition; thirdly the toolbox was originally written to emphasise clarity (for teaching purposes) but somewhat at the expense of performance (the authors now feel that a better balance between clarity and performance can be achieved and therefore all of the MATLAB code has been rewritten); fourthly, the presentation of the text has been rewritten to provide a more logical and consistent presentation; fifthly, the comprehensive use of colour throughout the second edition provides opportunities to include topics that were more difficult to include in the first edition.

IntroductionGammaThe GOG ModelDevice-Independent TransformationCharacterisation Example of CRT DisplayBeyond CRT Displays

Arno Puder
Oren Antebi

Android is currently leading the smartphone segment in terms of market share since its introduction in 2007. Android applications are written in Java using an API designed for mobile apps. Other smartphone platforms, such as Apple's iOS or Microsoft's Windows Phone 7, differ greatly in their native application programming model. App developers who want to publish their applications for different platforms are required to re-implement the application using the respective native SDK. In this paper we describe a cross-compilation approach, whereby Android applications are cross-compiled to C for iOS and to C# for Windows Phone 7. We describe different aspects of our cross-compiler, from byte code level cross-compilation to API mapping. A prototype of our cross-compiler called XMLVM is available under an Open Source license.

Digital imaging has become a powerful tool for the characterization and quality control of foodstuff. Because of the need to automate processes, faster tools are needed and Computer Vision is a good alternative to chemical analysis of many products in quality control. Appearance of grape seeds and grape berries change during the ripeness. These changes are closely related to the chemical composition, especially phenolics, which are very important compounds due to their implications on the intensity and stability of red wine colour. In this study, a complete characterization of grape seeds and grape berries by digital image analysis is described. The size of grapes and the veraison has been determined by image analysis and it has been also established an objective Browning Index of seeds. Morphological differences between varieties were studied by applying discriminant analysis models which allowed us to classify the grape seeds with high accuracy.

Gunther Wyszecki
W. S. Stiles

This paperback reprint of a classic book deals with all phases of light, color, and color vision, providing comprehensive data, formulas, concepts, and procedures needed in basic and applied research in color vision, colorimetry, and photometry.

Source: https://www.researchgate.net/publication/259119272_Using_the_mobile_phone_as_Munsell_soil-colour_sensor_An_experiment_under_controlled_illumination_conditions

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