Understanding the Effect of Smartphone Cameras on Estimating Munsell Soil Colors from Imagery Ricky Sinclair

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Understanding the Effect of Smartphone Cameras

on Estimating Munsell Soil Colors from Imagery

Ricky Sinclair

School of Computing, Mathematics and Engineering

Charles Sturt University

NSW, Australia

rickysinclair@gmail.com

Muhammad Ashad Kabir1,2

1School of Computing, Mathematics and Engineering

2Gulbali Institute for Agriculture, Water and Environment

Charles Sturt University

NSW, Australia

akabir@csu.edu.au

Abstract—The Munsell soil color chart (MSCC) is an im-

portant reference for many professionals in the area of soil

color analysis. Currently, the functionality to identify Munsell

soil colors (MSCs) automatically from an image is only feasible

in laboratories under controlled conditions. To support an app-

based solution, this paper explores three research areas including:

(i) identifying the most effective color space, (ii) establishing the

color difference calculation method with the highest accuracy and

(iii) evaluating the effects of smartphone cameras on estimating

the MSCs. The existing methods that we have analysed have

returned promising results and will help inform other researchers

to better understand and develop informed solutions. This study

provides both researchers and developers with an insight into the

best methods for automatically predicting MSCs. Future research

is needed to improve the reliability of results under differing

environmental conditions.

Index Terms—Smartphone, Munsell soil colors, image process-

ing, color space, color difference calculation

I. INTRODUCTION

Many theories have developed over the years in attempts to

better explain how colors work and how best to calculate and

describe color differences. In 1913, Albert Munsell introduced

the Atlas of the Munsell color system, arranging color into the

tristimulus of hue, value, and chroma [1]. The Munsell color

system has enabled professionals to bridge the disciplines

of art and science and is the basis of many professional

applications today such as food science [2], dentistry [3],

printing [4], painting [5], and soil science [6]. Soil colors are

most easily measured by comparison with a Munsell soil color

chart (MSCC), an adaptation of the Munsell color space that

includes only the values needed for soil colors, about one-ﬁfth

of the complete series of colors. The organisation of the MSCC

is the same as the Munsell soil color system hue, value, and

chroma (HVC).

Color is one of the most noticeable features of soil and

is very important in the assessment and classiﬁcation of soil

properties. It can also provide valuable insights into the soil

environment, often being used for predictively determining

soil properties and the land’s appropriateness for uses. The

prediction of soil color is also of national importance in

Australia, as current land management techniques are reducing

the levels of nutrients in the soil at alarming rates [7].

©IEEE, accepted to publish in DICTA 2022 proceedings

Several researchers have developed varying techniques to

predict Munsell soil colors (MSCs) from a range of tech-

nologies, and the opinions of each vary when it comes to the

selection of the best technologies to utilise in this endeavour

[8]–[12]. Whilst current research discusses the method of

color detection techniques, the justiﬁcation in selecting each

individual element in these methods is lacking bringing into

question the processes for automatic MSCs predictions.

In this paper, our primary focus is understanding and

analysing the various color spaces (i.e., RGB, CIELCh,

CIELab, XYZ, and CMYK) and the color difference calcu-

lation methods (i.e., CIEDE1976, CIEDE1994, CIEDE2000,

and CMC) for the MSCs. Consequently, the color difference

calculation methods have examined to enable a working theory

for estimating MSCs from smartphone captured images. The

effect of different smartphone cameras is also analysed and

discussed. The ﬁndings from this paper established CIELab

is the most appropriate color space using the CIEDE2000

color difference calculation method combined with the D65

illuminant.

II. METHODOLOGY

A review of the color spaces used for the conversion of the

MSCC and the associated color difference calculation methods

will be investigated and evaluated for their effectiveness. This

review will enable the researchers to select the best color space

and conversion algorithms.

Furthermore, the effects of smartphone cameras on esti-

mating the MSCs will be explored. More speciﬁcally, an

analysis of the differences between smartphone cameras and

the accuracy of colors and images captured across devices will

be explored. Color correction techniques will also be assessed

for their effectiveness in increasing the reliability and accuracy

of Munsell color identiﬁcation. This study will focus on being

able to accurately and reliably being able to match colors to

the MSCC from captured images.

Although, research in the area of Munsell soil color predic-

tion is extensive, there seems to be little justiﬁcation for the use

of the methodologies applied. For example, many researchers

focus on the devices [8], [10], [12] being used rather than

the conversion and calculation methods, taking these processes

for granted in their results. Fewer studies have compared these

arXiv:2210.06667v1 [cs.CV] 13 Oct 2022

methods for their reliability and effectiveness [11], [12]. There

remains the need for an analysis to be undertaken that develops

a cohesive solution incorporating a thorough discernment of

the devices, color space, color difference calculation methods,

effects of smartphone cameras (SPCs), and color correction

techniques used in the prediction of Munsell soil color values.

These key issues are detailed in the following subsections.

III. COLOR SPACES FOR MSCC

Mobile phones can only record image colors in RGB.

Munsell colors, therefore, need to be interpolated from RGB

to Munsell. However, a range of possible color spaces could

be utilised as a proxy between RGB, and the Munsell Soil

Color Chart, to improve the accuracy of samples. If a medium

is needed, then the question remains, which color space is

best to act as a medium between RGB and Munsell? The

RGB, CMYK, CIEXYZ, CIELCh and CIELab color spaces

were selected as possible mediums as this colorimetry data is

available from the Nix Pro 2 1which will allow a thorough

analysis of the effects that each color space has during the

conversion process.

RGB is composed of a tristimulus, in which the hue values

move from red through other colors and back to red [13].

This makes it difﬁcult to calculate distances from one color

to another in the RGB color space. To establish a numerical

description of these colors, the categories must be transformed

to a value. Each color system has various advantages when

converting from a Cartesian color coordinate system to Mun-

sell color descriptors. The choice of the right hue also becomes

more difﬁcult the lower the Chroma of a color [14]. Since

the distance between the adjacent Hue chart gets ever smaller

the nearer they approach the axis. Soils frequently contain

subdued colors (i.e. low Chromas), increasing the complexity

in the determination of Hue.

One of the main issues is that these values can be very close

to one another, and any slight change in one of the tristimulus

results in the hue of the Munsell value changing. This can

create problems for the repeatability of results and, therefore,

affect the accuracy of the readings. A 3D spatial model of

the interpolations between the color space and the MSCC was

developed to analyse the most effective color space. What we

are looking for here is a clear separation of the ‘clumping’ of

results. This ‘clumping’ shows that the different hues in the

MSCC are too closely aligned, meaning that any small changes

in the tristimulus of a sample, converted to a particular color

space, results in a large movement in the MSCC to another

hue rather than an adjacent chip in the MSCC.

The MSCC contains the color chips from 14 hues in total.

When analysing the potential use of a color space as a medium

creating a 3D spatial model for all of these chips would

become quite populated and difﬁcult to read. Therefore, the

most relevant hues were determined as applied to Australian

soils. The Australian national site collation researched the most

prominent hue colors for samples collected around Australia

1https://www.nixsensor.com/nix-pro/

[15]. Over 680,000 observations were recorded from topsoils

recorded at a depth shallower than 5cm from the surface. This

data displays a trend that the primary soil colors in Australia

are: 5Y, 2.5Y, 10YR, 7.5YR, 5YR, 2.5YR and 10R. This

essentially removes gleys, bright red colors, and whites. This

is a strong justiﬁcation that the research in this area focuses

on these hues from the MSCC.

Munsell notations are not always unambiguous. Apart from

the human error and the individual color perception, the deter-

minations allow for some uncertainty due to the closeness of

the values, especially when more difﬁcult materials, like soils,

are valued. To investigate the relevance of this uncertainty,

each color space was explored and discussed as a potential

medium for the interpolation process.

The Munsell color space is based on a three-dimensional

model in which each color is comprised of the tristimulus

of hue (color type), value (lightness/darkness) and chroma

(color saturation). Hue, value and chroma are also annotated

as (HVC).

The Munsell color space gives us an intuitive designation

to express our perception of color and its changes, similar to

that of the human eye [16]. However, the subjective nature of

Munsell color charts and the limited number of color chips

can impose restrictions on precise color measurements [17],

[18]. Whilst the MSCC remains the standard color space for

analysing soil. As discussed earlier, this color space is not

natively readable by electronic devices and relies upon varied

interpolation methods. In this section, we aim to determine the

best color space to store soil samples from a SPC and then

compare them to the MSCC color chips via interpolation.

RGB is the basic color space for digital cameras and

computer displays that use red, green, and blue to create the

required color. Therefore, the RGB color space is the obvious

choice for software systems due to its ease of integration. For

this reason, [13] argued in favour of the RGB system to be

used to convert Munsell soil colors. In the RGB system, all

three highly correlated bands determine illumination intensity

jointly, which is the major disadvantage of the system [19].

This means that the conversion of Munsell codes to RGB is

not appropriate at certain high values and chroma levels as the

range of RGB values is in effect not large enough to cover all

of the possible Munsell values. Because of this disadvantage,

other color spaces are considered a better choice for Munsell

interpolation [10].

The RGB color space would be the most convenient color

space to use for Munsell soil color comparison as it would

require no medium and can be converted directly to match the

MSCC. Indeed, [20] recommended using RGB values when

utilising a smartphone camera to process color images of soil

samples as RGB is the native color space for these devices.

Similarly, RGB was utilised by the majority of researchers

[20]–[26]. However, several studies incorporated a combina-

tion of color spaces to determine which method returned the

best results [8], [27], [28]. [23] argues that RGB measurements

from the mobile phone obtained good results, but using a

more stable color space, such as CIELab, might improve the

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UnderstandingtheEffectofSmartphoneCamerasonEstimatingMunsellSoilColorsfromImageryRickySinclairSchoolofComputing,MathematicsandEngineeringCharlesSturtUniversityNSW,Australiarickysinclair@gmail.comMuhammadAshadKabir1,21SchoolofComputing,MathematicsandEngineering2GulbaliInstituteforAgriculture,Waterand...

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Understanding the Effect of Smartphone Cameras on Estimating Munsell Soil Colors from Imagery Ricky Sinclair

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