imaging techniques for the detection of stored product pests

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Applied Entomology and Zoology ISSN 0003-6862Volume 49Number 2 Appl Entomol Zool (2014) 49:201-212DOI 10.1007/s13355-014-0254-2

Imaging techniques for the detection ofstored product pests

Mohd Abas Shah & Akhtar Ali Khan

1 23

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REVIEW

Imaging techniques for the detection of stored product pests

Mohd Abas Shah • Akhtar Ali Khan

Received: 18 November 2013 / Accepted: 19 February 2014 / Published online: 7 March 2014

� The Japanese Society of Applied Entomology and Zoology 2014

Abstract Stored grains are subject to deterioration and

losses through various factors, but mainly insects and

fungi. Various techniques are employed to detect stored

product pests; however, there is an urgent need for an

industrial-scale on-line detection technique. Near-infrared

hyperspectroscopic imaging and soft X-rays have shown

the potential for real-time application. These techniques are

particularly effective for detecting internal infestations of

stored grains. The digital images of the scanned objects are

analyzed for various spectral and image features using

statistical techniques such as complex multivariate tools.

Classification accuracies as high as 80–100 % have been

achieved for various pest and grain combinations. Dual-

energy X-rays have been shown to detect the concealed

eggs of stored product insect pests. The main threats to

stored cereals come from Aspergillus spp., Penicillium

spp., and Fusarium spp., which may produce mycotoxins.

These imaging techniques have shown good results in the

detection of fungal infections of stored grain.

Keywords Soft X-rays � Sitophilus oryzae � Internal

infestation � Mycotoxins � Hyperspectroscopy

Introduction

Ten to thirty percent of the grains produced worldwide are

lost every year due to insect, rodent, and microbial damage

(Singh et al. 2009). Insect damage to grains results in loss

of weight, nutrients, and germination ability, and increased

susceptibility to contamination by fungi. This deterioration

and contamination by insects and insect parts downgrades

the grains and lowers their market value. Insect infestation

of wheat adversely affects the baking quality of the flour

(decreased loaf volume, compact and inelastic crumb,

bitter taste, and off-flavors) (Sanchez-Marinez et al. 1997),

and insect fragments are a major concern for the wheat-

flour milling industry because of legal contamination limits

(Edwards et al. 1991; Perez-Mendoza et al. 2003). Internal

feeding insects are considered the most damaging and most

difficult to detect. The immature stages of these insects

develop inside the kernel and then the adults emerge and

leave an exit hole, producing an insect-damaged kernel

(Pearson et al. 2003). Insects produce heat and moisture

due to their metabolic activity, which can cause the

development of insect-induced localized hotspots in grain

bins and the spoilage of grain by fungi. Fungal growth

during the storage of grain is a serious cause of spoilage in

grains. Fungal damage to cereal grain causes germination

loss, discoloration, dry matter loss, increase in free fatty

acids, heating, mustiness, and the production of mycotox-

ins (Khan et al. 2012).

Most traditional insect detection methods, such as grain

floatation, the Berlese funnel method, probes and traps, and

carbon dioxide and uric acid measurements, have one or

more drawbacks; they may be subjective, destructive,

inaccurate, time-consuming, and/or unable to detect inter-

nal insect infestation Neethiranjan et al. (2007c). Methods

such as enzyme-linked immunosorbent assays (ELISA),

electronic nose, acid hydrolysis, electrical conductance,

acoustic impact emissions, and molecular techniques have

also been used for insect detection in grains (Singh et al.

2009). Most of these methods are, however, unable to

detect low-level internal infestations and they have not

M. A. Shah (&) � A. A. Khan

Division of Entomology, Sher-e-Kashmir University of

Agricultural Sciences and Technology of Kashmir, Shalimar,

Srinagar 190 025, India

e-mail: [email protected]

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Appl Entomol Zool (2014) 49:201–212

DOI 10.1007/s13355-014-0254-2

Author's personal copy

shown the potential for automated inspection. An objec-

tive, nondestructive, rapid, and accurate method for the

detection of insect-damaged grain at grain-handling facil-

ities is required. Imaging techniques have shown some

potential in this regard. Techniques such as X-ray imaging,

magnetic resonance imaging (MRI), and near-infrared hy-

perspectroscopy have been explored for the nondestructive

evaluation of infested stored products. Chambers et al.

(1984) demonstrated that the hidden infestation of wheat

by the granary weevil (Sitophilus granarius) can be

detected by nuclear magnetic resonance. However, the

sensitivity achieved was very low, and there have been no

major attempts to use MRI to detect stored product pests

since then. The other two advanced methods—near-infra-

red (NIR) hyperspectroscopy and X-ray imaging—have

shown the potential for real-time application. The present

paper provides a review of the principles of X-ray imaging

and NIR hyperspectroscopy along with an overview of

attempts to use these techniques for the real-time detection

of insects and fungi in stored products.

Near-infrared hyperspectral imaging (NIR-HSI)

Principle and applications

Near-infrared spectroscopy has been used to evaluate the

quality of many cereal grains, including for the detection of

insect and insect parts in whole-grain and ground samples

(Maghirang et al. 2003; Singh et al. 2006). The NIR

technique uses the spectral differences between healthy and

infested kernels caused by differences in the chemical

compositions of healthy and damaged kernels to achieve

discrimination. Near-infrared spectra obtained from NIR

instruments do not give spatial information, so insects

hidden inside kernels cannot be located in the grains.

However, hyperspectral imaging provides spectral infor-

mation in a spatially resolved manner, meaning that hidden

insects and internal damage can be located (Gowen et al.

2007). Conventional NIR spectroscopic instruments are

considered point-based scanning instruments, as they pro-

vide a spectrum of the target sample but no information on

the spatial distribution of the chemical composition of the

sample. Spatial information is important when monitoring

the grain, as it can be used to extract the chemical map of

the sample from a hypercube (Kaliramesh et al. 2013).

Hyperspectral imaging, also known as chemical or spec-

troscopic imaging, is an emerging technique that integrates

conventional imaging and spectroscopy to attain both

spatial and spectral information from an object. Hyper-

spectral imaging, like other spectroscopy techniques, can

be carried out in reflectance, transmission, or fluorescence

modes. The mapping can be examined visually and

objectively by grain inspectors (Singh et al. 2009). A novel,

nondestructive technology, hyperspectral imaging com-

bines both visible and near-infrared wavelength informa-

tion with image information. This approach can provide

more detection information, including internal structure

characteristics, morphology information, and the chemical

composition, as compared with a single machine vision

technology or a spectroscopic analysis technology (Huang

et al. 2013). A simple NIR-HSI setup is shown in Fig. 1.

Hyperspectral images are made up of hundreds of con-

tiguous wavebands for each spatial position in the target

studied. Consequently, each pixel in a hyperspectral image

contains the spectrum of that specific position. The

resulting spectrum acts like a fingerprint that can be used to

characterize the composition of that particular pixel.

Hyperspectral images, known as hypercubes, are three-

dimensional blocks of data comprising two spatial dimen-

sions and one wavelength dimension (Fig. 2). The hyper-

cube permits the visualization of the biochemical

Fig. 1 Near-infrared

hyperspectral imaging system. 1

Grain sample, 2 liquid-crystal

tunable filter (LCTF), 3 lens, 4

NIR camera, 5 copy stand, 6

illumination (halogen-tungsten

lamp), 7 data processing system.

(Reproduced with permission

from Elsevier Ltd.)

202 Appl Entomol Zool (2014) 49:201–212

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constituents of a sample; these are separated into particular

areas of the image since regions of a sample with similar

spectral properties have similar chemical compositions (Lu

and Chen 1998). There are two conventional ways to

construct a hypercube, known as the ‘‘staring imager’’

configuration and ‘‘pushbroom acquisition’’ (reviewed by

Gowen et al. 2007). There are numerous techniques for

analyzing hyperspectral data, all of which aim to reduce the

dimensionality of the data while retaining important

spectral information with the power to classify important

areas of a scene. NIR-HSI for the detection of stored grain

pests is based on the absorption of electromagnetic wave-

lengths in the range 780–2500 nm (Dowell et al. 1999).

Hyperspectral images are directly analyzed for wavelength

selection using multivariate image analysis (principal

component analysis, PCA) without using spectral data to

overcome system variations between the instruments

(Singh et al. 2010). Usually, the wavelengths correspond-

ing to the highest factor loadings of the first two or three

principal components are selected for image feature

extraction. The wavelength region 1100–1300 nm corre-

sponds to the first and second overtones and a combination

band for C–H, and wavelengths in this region are signifi-

cant as they are associated with absorption by starch

molecules (Singh et al. 2009). Absorption at 1135 and

1325 nm was found to be significant by Maghirang et al.

(2003) for the detection of insects in wheat. Dowell et al.

(1999) related the peak at 1330 nm to the cuticular lipids

present in Sitophilus oryzae (L.), whereas the wavelength

regions 1130–1200 and 1300–1400 nm were found to be

significant by Baker et al. (1999) when identifying

parasitized S. oryzae in wheat. The wavelength 1440 nm

was found to be important for insect detection by Toews

et al. (2007). The first step is a reflectance calibration that

is carried out to account for the background spectral

response of the instrument and the ‘‘dark’’ camera

response. Hypercube classification enables the identifica-

tion of regions with similar spectral characteristics. Due to

the large size of a hypercube (which can exceed 50 MB,

depending on image resolution, spectral resolution, and

pixel binning), complex multivariate analytical tools such

as principal component analysis (PCA), partial least

squares (PLS), linear discriminant analysis (LDA), Fisher’s

discriminant analysis (FDA), multi-linear regression

(MLR), and artificial neural networks (ANN), or a com-

bination thereof, are usually employed for classification

(Gowen et al. 2007). Image processing is carried out to

convert the contrast developed in the classification step into

a picture depicting the component distribution. Grayscale

or color mapping with intensity scaling is commonly used

to display compositional contrast between pixels in an

image. Image fusion, in which two or more images at

different wavebands are combined to form a new image

(Pohl 1998), is frequently implemented to provide even

greater contrast between the distinct regions of a sample

(Liu et al. 2007; Park et al. 2006). The aforementioned

steps that are followed when analyzing hyperspectral

images are outlined in Fig. 3. Hyperspectral imaging

technology has been utilized in agriculture, such as for

assessing the internal quality of fruit (Huang and Lu 2010;

Lu and Peng 2006; Naganathan et al. 2008), identifying

internal damage to pickling cucumbers (Ariana and Lu

2008), and acquiring crop growth information (Ramalin-

gam et al. 2005; Ruiz-Altisent et al. 2010), and, recently, in

the detection of stored product pests.

NIR-HSI for the detection of stored product insects

The NIR-HSI system has been widely used for the detec-

tion of coleopteran storage pests of rice and wheat (Dowell

et al. 1998; Huang et al. 2013; Singh et al. 2009; Strelec

et al. 2012). Kaliramesh et al. (2013) attempted the

detection of bruchids infesting mung beans using this

approach, while Dowell et al. (1998) utilized it to identify

wheat kernels infested with Angoumois grain moths,

among other species. In these studies, statistical and his-

togram features were extracted at wavelengths corre-

sponding to highest principal component factor loadings,

and excellent but variable discrimination was achieved

using various multivariate analysis tools. Dowell et al.

(1998) scanned wheat kernels infested internally with lar-

vae of three primary insect pests of grain: the rice weevil,

S. oryzae; the lesser grain borer, Rhyzopertha dominica

(F.); and the Angoumois grain moth, Sitotroga cerealella

Fig. 2 Schematic representation of a hyperspectral imaging hyper-

cube showing the relationship between spectral and spatial dimen-

sions. (Reproduced with permission from Elsevier Ltd.)

Appl Entomol Zool (2014) 49:201–212 203

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(Olivier), to determine differences in absorption due to the

presence of larvae using the single kernel characterization

system. This automated system was able to differentiate

uninfested kernels from kernels infested with larvae of all

three species. Moisture content, protein content, and wheat

class did not affect classification accuracy. The calibration

included spectral characteristics in the wavelength ranges

1000–1350 and 1500–1680 nm. Larval size was a factor in

the sensitivity of the system, with third- and fourth-instar

rice weevils being detected with 95 % confidence. Singh

et al. (2009) extracted statistical image features (maximum,

minimum, mean, median, standard deviation, and variance)

and histogram features at 1101.69 and 1305.05 nm from

images of healthy wheat kernels and wheat kernels visibly

damaged by S. oryzae, R. dominica, Cryptolestes ferru-

gineus, and Tribolium castaneum. The extracted features

were given as input to statistical discriminant classifiers

(linear, quadratic, and Mahalanobis) for classification, and

the linear and quadratic discriminant analysis classifiers

correctly classified 85–100 % healthy and insect-damaged

wheat kernels. Uninfested mung bean kernels and kernels

infested with different stages of Callosobruchus maculates

F. were imaged within the wavelength region

1000–1600 nm at 10-nm intervals by Kaliramesh et al.

(2013). The wavelengths corresponding to the highest

principal component (PC) factor loadings (1100, 1290, and

1450 nm) were considered to be significant. Average

classification accuracies of more than 85 and 82 % were

obtained by using nonparametric statistical classifiers to

identify uninfested and infested mung bean kernels,

respectively, making use of statistical and histogram fea-

tures. Kaliramesh et al. (2013) also reported that mung

bean kernels with pupal and adult stages of infestation had

higher classification accuracies than those infested with

egg and larval stages, using both classifiers.

Other than reflectance, other modes of NIR-HSI have

also been explored for storage pest detection, with equiv-

alent efficacy. Huang et al. (2013) used hyperspectral

transmission images, while Singh et al. (2010) made

additional use of a color imaging system. Strelec et al.

(2012) successfully discriminated between infested and

uninfested wheat kernels using fluorescence properties and

the UV/VIS spectral characterization of various wheat

grain extracts. Hyperspectral transmission images were

acquired from normal and insect-damaged vegetable soy-

beans over the spectral region between 400 and 1000 nm

(Huang et al. 2013). Four statistical image features (mini-

mum, maximum, mean, and standard deviation) were

extracted from the images for classification and given as

input to a discriminant classifier. The support vector data

description (SVDD) classifier achieved 100 % calibration

accuracy. SVDD achieved accuracies of 97.3 and 87.5 %

for normal and insect-damaged samples, respectively, with

an overall classification accuracy of 95.6 %. Singh et al.

(2010) scanned healthy wheat kernels and wheat kernels

damaged by S. oryzae, R. dominica, C. ferrugineus, and T.

Image processing to convert

the contrast developed by the classification step into a picture depicting component

distribution.

Classification to enable identification of regions with similar spectral characteristics

Hyperspectral image acquisition

Selection of wavelengths corresponding to the highest factor loadings using

PCA

Reflectance Calibration

Fig. 3 Schematic diagram of

the hyperspectral imaging

process


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castaneum using an NIR hyperspectral imaging system

(700–1100 nm wavelength range) and a color imaging

system. The dimensionality of the hyperspectral data was

reduced and statistical and histogram features were

extracted from NIR images of significant wavelengths and

given as input to three statistical discriminant classifiers

(linear, quadratic, and Mahalanobis) and a back-propaga-

tion neural network (BPNN) classifier. A total of 230

features (color, textural, and morphological) were extracted

from the color images, and the most discriminatory features

were selected and used as input to the statistical and BPNN

classifiers. The quadratic discriminant analysis (QDA)

classifier gave the highest accuracy, correctly identifying

96.4 % healthy and 91.0–100.0 % insect-damaged wheat

kernels using the top 10 features from 230 color image

features combined with hyperspectral image features.

Strelec et al. (2012) reported that instrumental analysis of

the spectral characteristics of grain extracts could be used

as a method of determining grain infestation. They used

various extracts of wheat grains infested by S. granarius

larvae to detect infested grains using four different types of

extracts: tris–HCl, aqueous isopropanol, methanol, and

aqueous trichloroacetic acid. The extracts of uninfested and

infested wheat grains were examined for their fluorescence

properties and UV/VIS spectral characteristics, while the

chemometric analysis included determinations of protein,

reactive amino groups, polyphenols, and soluble sugar

content. Analysis of extracts revealed significant differ-

ences in UV/VIS spectral characteristics, the levels of

reactive amino groups, and polyphenol contents between

infested and uninfested grains. Thus, they concluded that

instrumental analysis of the spectral characteristics of grain

extracts could be used as a method of determining grain

infestation.

Although all modes of NIR-HSI have been shown to be

accurate enough for field use, most of the work performed

so far has focused on reflectance imaging. Hyperspectral

characterization of grain extracts may not find much favor,

being destructive in nature. There are differential but

nonsignificant variations in detection efficiency among

various stored grain pest species, but calibration for indi-

vidual species could be useful. The same logic holds for the

detection of various developmental stages of the pest spe-

cies, particularly the eggs and early larval instars.

X-ray imaging

Principle

X-rays are defined as electromagnetic radiation with wave-

lengths ranging from about 0.01 to 10 nm. They are pro-

duced when high-energy electrons strike a target material,

typically tungsten. An X-ray tube is similar in design to a

light bulb, except that the electrons that are released from the

heated filament are subjected to a high voltage, causing them

to accelerate to high energies and then strike the target. As

these high-energy electrons decelerate in the target material,

electrons of target atoms are excited to higher energy levels

and then decay to their ground states, resulting in the emis-

sion of X-ray photons. The size of the target area over which

X-rays are generated is called the focal spot size, and this size

influences the characteristics of the imaging system. The

X-rays themselves have two characteristics that are impor-

tant in the operation of the X-ray machine: energy and cur-

rent. The former defines the penetrating power of the X-ray

beam, while the latter is associated with the number of X-ray

photons that are generated. A balance is required between the

energy and current, and this has consequences for the

resulting image quality (Renu and Chidanand 2013). The

shorter the wavelength of the X-ray, the greater its energy

and penetrating capacity. The shorter wavelengths—those

closer to or overlapping with the gamma ray region—are

called hard X-rays. X-rays can penetrate many objects due to

their high energies. However, there is variability in their

ability to penetrate through different materials due to dif-

ferences in the properties of those materials. Photons in an

X-ray beam are transmitted, scattered, or absorbed when

they pass through an object (Curry et al. 1990). Radiography

seeks to capture differences in X-ray transmission caused by

variations in material properties in the form of visual contrast

in the image. This contrast can be a measure of the spatial and

quantitative distributions of certain material(s) within a

composite of materials. Electromagnetic waves with wave-

lengths ranging from 0.1 to 10 nm and corresponding ener-

gies of about 0.12–12 keV are called soft X-rays. Due to their

low penetrating power and ability to reveal internal density

changes, soft X-rays are more suitable for use with agricul-

tural products (Renu and Chidanand 2013).

X-ray imaging is an established technique for detecting

strongly attenuating materials that has been applied to a

number of inspection applications within the agricultural

and food industries. Soft X-rays are extensively used in the

fruit and vegetable industry to determine maturity levels, to

detect internal voids, defects, and insect damage, and to

identify foreign material in foods (Keagy et al. 1995).

X-ray techniques are used to detect any kind of insect pest

in a variety of agricultural products, such as grains, fruits,

vegetables; insects hidden in the stored grains are of par-

ticular importance (Nawrocka et al. 2012).

Development of X-ray imaging techniques

X-rays have been used extensively to detect insect infes-

tations in cereal grains and to study the developmental

behavior of insects (Haff and Slaughter 1999; Keagy and


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Schatzki 1993; Mills and Wilbur 1967; Milner et al. 1950,

1952; Schatzki and Fine 1988; Sharifi and Mills 1971a, b;

Stermer 1972). Except for a few studies, X-ray radiographs

are generally developed manually and then inspected to

identify insect-infested grain kernels (Karunakaran et al.

2004a). This approach is widely used as a test reference

method (Fornal et al. 2007). Existing X-ray techniques

enable the classification of at least four stages of insect

development by measuring the area occupied by the insect,

and accurate classification is also possible based on the

visible insect morphology (Pearson et al. 2003). According

to Brader et al. (2002), the time needed for a complete

analysis is about 2.5 h, whereas the immunoassay method

and the cracking and flotation method as a whole take less

than 1 and 1.5 h, respectively. The possibility of signifi-

cantly shortening the X-ray procedure has been suggested

by Haff and Slaughter (2004), who proposed the use of

real-time digital imaging instead of X-ray film for dis-

criminating infested kernels. However, film observations

gave better accuracy (3 % error rate) than digital images

(11.7 %) for discriminating among infestations by third-

larval instars, while the error was less than 1 % when using

either method to identify more advanced stages of larval

development. The industrial separation of externally living

insects is not a problem; for instance, simple sieving can

realize this goal well. When wheat grains are infested by

internal feeders such as granary weevils, the most impor-

tant problem to solve is how to detect the hidden stages of

larval development, particularly just after oviposition,

inside the kernel. Haff and Pearson (2007) developed an

automatic recognition algorithm to detect wheat kernels

infested with the granary weevil S. granarius and olives

infested with the olive fly Bactrocera oleae (Gmelin),

using X-ray imaging. In that study, 64 features were

extracted from each image and the discriminant analysis

routine was used to test every possible combination of

these features. The algorithm yielded results comparable to

those obtained by human subjects evaluating digitized

X-ray film images. However, this algorithm did not prop-

erly classify images containing early larval stages. Wheat

kernels artificially infested by red flour beetle were

X-rayed by Karunakaran et al. (2004a), and an algorithm

was developed to extract features based on histogram

groups, textural features, and histogram and shape

moments from the X-ray images of wheat kernels. In that

study, the identification of sound and infested kernels was

achieved by means of the DISCRIM procedure using sta-

tistical classifiers and back-propagation neural network

(BPNN) analysis, but detection of early-stage infestations

was poor. Keagy and Schatzki (1993) developed an algo-

rithm for the machine recognition of granary or maize

weevil damage in wheat kernels. The algorithm used

convolution masks to look for intersections at eight

orientations. The intersections were defined as additional

edges and lines seen at angles to the crease on binary

images of infested kernels. In sound kernels, the central

crease was represented by a line on the binary image.

Using this algorithm, it was possible to identify infestation

by larger stages only. Fornal et al. (2007) applied a three-

stage algorithm to detect eggs and internal stages of gra-

nary weevil in wheat grains from soft X-ray images. It used

segmentation of image objects and a local equalization

filter to extract image detail. They also proposed equations

for calculating the approximate date of infestation. Soft

X-rays of granary weevil infested wheat kernels revealed

that correlation coefficients for the number of objects and

the time of infestation could explain more than 85 % of the

variation in the relationship examined, while the relation-

ship between instrumental scores and the time after infes-

tation could explain more than 90 % of the data variability

(Fornal et al. 2007).

Soft X-ray imaging for the detection of stored product

insects

Soft X-rays have been widely used for the detection of

internal infestation of wheat kernels (Karunakaran et al.

2003, 2004a, b, c). In such methods, X-ray images are

analyzed in order extract features using histogram groups,

textural features, and histogram and shape moments. Dis-

crimination of infested and uninfested kernels and of ker-

nels containing various growth stages of the pest insects is

achieved by using various parametric and nonparametric

statistical classifiers and four-layer BPNN classifiers with

variable classification accuracies. Karunakaran et al.

(2003) reported that there was no significant difference

between the classifiers used for the identification of unin-

fested wheat kernels and those infested by rice weevils (S.

oryzae). More than 95 % of uninfested kernels and kernels

infested by larval stages were correctly identified by all of

the classifiers. Wheat kernels infested by pupae–adults and

insect-damaged kernels were identified with more than

99 % accuracy by the classifiers. Similarly, no significant

difference was found between the statistical and neural

network classifiers for the identification of uninfested

wheat kernels and those infested by T. castaneum (Karu-

nakaran et al. 2004a). Uninfested kernels and those infested

by four larval instars were correctly identified with clas-

sification accuracies of more than 73 and 86 % by the

statistical classifiers and BPNN, respectively. In a study of

R. dominica infested wheat kernels, Karunakaran et al.

(2004b) reported that BPNN correctly identified all unin-

fested and infested kernels and more than 99 % of the

infested kernels. The classification accuracies determined

by the BPNN were higher when using all 57 features than

when using the histogram and textural features separately.


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The BPNN performed better than the parametric and

nonparametric classifiers in the identification of uninfested

kernels and of kernels infested by different larval stages of

the insect. In a similar study of C. ferrugineus infested

wheat kernels, algorithms were developed to extract a total

of 57 features using histogram and shape moments, and

textural features using co-occurrence and run length matrix

methods. The extracted features were used to identify

uninfested and infested kernels using statistical classifiers

and a four-layer BPNN. More than 75.3, 86.5, and 95.7 %

of the sound kernels, of the kernels infested by larvae, and

of kernels infested by pupae–adults, respectively, were

correctly identified by the parametric classifier, nonpara-

metric classifier, and BPNN using all 57 features. The

study revealed that there were no significant differences

between the percentages of sound kernels identified by the

three classifiers, but the parametric classifier and BPNN

identified significantly higher percentages of infested ker-

nels, and identification percentages of infested kernels were

higher when using textural features or all 57 features,

rather than when using only the histogram features

(Karunakaran et al. 2004c).

Toews et al. (2006) explored the possibility of using

computed tomography (CT) as an alternative imaging

technique, while Fornal et al. (2007) and Neethirajan et al.

(2007a) employed dual-energy X-ray imaging techniques

as an alternative detection technique to simple transmission

X-ray imaging. CT, an imaging technique commonly used

to diagnose internal human health ailments, uses multiple

X-rays and sophisticated software to recreate a cross-sec-

tional representation of a subject. The use of this technique

to image hard red winter wheat samples infested with

pupae of S. oryzae was investigated by Toews et al. (2006).

Samples were imaged in a plastic tube containing 0, 50, or

100 infested kernels per kg of wheat. Interkernel spaces

were filled with corn oil so as to increase the contrast

between voids inside kernels and voids among kernels. The

average detection accuracy for five infested kernels per

100 g samples was 94.4 ± 7.3 %, whereas the average

detection accuracy for ten infested kernels per 100 g was

87.3 ± 7.9 %. The detection accuracy for ten infested

kernels per 100 g was slightly less than that for five

infested kernels per 100 g because some of the infested

kernels overlapped with each other or there were air bub-

bles in the oil. Thus, CT could be explored as an alternative

imaging technique for the detection of stored product pests.

Fornal et al. (2007) showed that, after introducing some

corrections for the working parameters of the equipment

used and modifying the digital image analysis used, the

soft X-ray method is suitable for the accurate detection of

granary weevil eggs laid in wheat kernels provided that at

least 5 days have elapsed since oviposition. The dual-

energy X-ray imaging technique is an alternative to simple

transmission X-ray imaging. The former has the ability to

reveal the internal density changes of a scanned object by

exploiting differences in how the scanned material interacts

with X-rays at different energies. Neethirajan et al. (2007a)

studied the feasibility of dual-energy X-ray image analysis

to classify vitreousness in durum wheat at moisture con-

tents of 12, 14, and 16 %. Neural network classifiers cor-

rectly classified vitreous and nonvitreous kernels with

93 % accuracy. The statistical classifiers provided 89 %

accuracy for vitreous and nonvitreous kernels. Fornal et al.

(2007) reported that the soft X-ray method is suitable for

the accurate detection of granary weevil (S. granarius)

eggs laid in wheat kernels if at least 5 days have elapsed

since oviposition. The correlation with time of infestation

showed that the object image was strongly determined by

the infestation period (r = 0.9381, p \ 0.05), as the

number of plugs and feeding tunnels increased during

infestation. Nawrocka et al. (2012) used soft X-ray imaging

to discern mass loss from wheat kernels infested by the

granary weevil. The mass loss strongly depended on the

life stage of the pest insect. The mass loss was calculated

from X-ray images taken 20–66 days after infestation using

a grayscale that accurately assigned the infested kernels to

different classes of infestation level using linear and

polynomial regression equations. The polynomial curve for

mass loss can be used to give an indication of the time and

place of infestation (Nawrocka et al. 2012).

Standardization of X-ray imaging

X-rays have two characteristics that are important in the

operation of an X-ray machine: energy and current. The

energy refers to the maximum energy that an X-ray photon

can possess when exiting the tube, and this defines the

penetrating power of the X-ray beam. The current, mea-

sured in mA, is associated with the number of X-ray

photons being generated. A balance is required between the

energy and current, and this has consequences for the

resulting image quality (Renu and Chidanand 2013).

Another important factor in the detection of stored product

pests is the duration of exposure. Studies on the standard-

ization of X-ray radiography methodology for the detection

of hidden insect infestations of different cereals were car-

ried out by Ramakrishnan et al. (2011). Results revealed

that the controllable input electrical parameters of the

X-ray machine—voltage, current, and exposure period

required for the detection of hidden insect infestation—

varied widely for different cereal seed materials. High

voltage and current were required for dense seed materials

to ensure adequate penetration of radiation compared to

light seed materials. Good image contrast was obtained

when the exposure period was between 20 and 25 s with all

combinations of the other electrical parameters for a


123


variety of cereals. The standardized X-ray input values

(voltage, current, and exposure period) for paddy seeds

were 15 kV, 12 mA, and 25 s, respectively, and these

resulted in good images that revealed the gross internal

contents of the seeds, facilitating the detection of hidden

infestations and the damage caused by the Angoumois

grain moth, S. cereallela. The standardized voltages, cur-

rents, and exposure periods for wheat, sorghum, and maize

seeds infested with the lesser grain borer (R. dominica)

were found to be 15 kV, 12 mA for 25 s; 20 kV, 6 mA, for

25 s; and 25 kV, 8 mA for 20 s, respectively. Adhesive

tape was found to be very useful for locating and picking

out the infested seeds from the healthy ones. In order to

elucidate the effect of X-ray exposure on seed quality,

Ramakrishnan et al. (2011) determined the germination

percentage of the exposed seed and reported that this per-

centage varied from 90 to 100 % for paddy, wheat, and

sorghum seeds and from 80 to 100 % for maize seeds when

different combinations of input values were employed,

indicating a negligible impact or no impact of soft X-rays

on the viability of the seeds.

Comparison of NIR-HSI and X-ray imaging

The soft X-ray method has the advantage over NIR spec-

troscopy of being potentially applicable to grain inspection

when the number of infested or insect-damaged kernels is

required. On the other hand, NIR spectroscopic analysis of

bulk samples has applications in grain management, such

as in association with fumigation, when the identification

of the insect species present is critical but the precise

quantification of infestations is not vital (Karunakaran et al.

2005). NIR-HSI approaches are, however, affected by

drawbacks such as the need to develop robust calibration

models, to perform preliminary processing of the collected

data, and to define and implement robust classification

logics. That said, an imaging system that works in the NIR

has several advantages over X-ray imaging, mainly in

terms of cost, simplicity, compactness, and safety.

Imaging storage fungi

The main threats to stored cereals come from Aspergillus

spp., Penicillium spp., and Fusarium spp., which may

produce mycotoxins such as aflatoxin, citrinin, xanthoqui-

nones, ochratoxin A, sterigmatocystin, and penicillic acid

(Narvankara et al. 2009). Though visible molds can be

removed from the kernels, mycotoxins cannot be removed

from the final products as they are usually integral to grain

kernels and are not degraded during processing. Therefore,

products made from infected grains can be poisonous to

consumers as well as to animals if used as feed. It can also

be dangerous to utilize the raw material after removing the

infected parts, as the toxins can spread to uninfected parts

by diffusion (Gourama and Bullerman 1995).

Analysis of the techniques currently in use

The implementation of proper control measures in combi-

nation with an efficient detection technique may help to

control fungal infections of grain. Chemical methods

(Gaunt et al. 1985; Sashidhar et al. 1988), immunological

methods (Kamphuis et al. 1989; Notermans and Kamphuis

1992), and those based on electrical impedance (Jarvis

et al. 1983), selective and differential media (Gourama and

Bullerman 1995), fungal volatiles (Scotter et al. 2005), the

most probable number (MPN) (Brodsky et al. 1982), the

Howard mold count (HMC), and the polymerase chain

reaction (PCR) (Manonmani et al. 2005) are currently used

to detect fungal infections. These methods are reliable, but

most are time-consuming and tedious since sample prepa-

ration requires a long time. The proteins and carbohydrates

of the cereals may interfere with the fungi, which may lead

to misinterpretation of the results. Visual inspection

methods or human-knowledge-based methods are sub-

jective, sometimes inconsistent, and slow, and chemical

methods are destructive and time-consuming (Mares 1993;

Neethirajan et al. 2007a). Both of these approaches are thus

not suitable for application to on-line inspection. Optical

methods are increasingly being investigated and exten-

sively applied in the development of accurate detection

techniques for the identification of damaged kernels.

Imaging technique analyses have been utilized to achieve

the goal of industrial-scale on-line inspection. There has

been increased interest from many researchers in machine

vision based technology to assess the physical properties of

grain. Those researchers have developed combined multi-

spectral image acquisition, processing, and analysis tech-

niques with advanced classification algorithms to detect

grain kernel characteristics such as color, texture, and

various types of damage (Bacci et al. 2002; Luo et al. 1999;

Ruan et al. 2001; Zayas et al. 1994). Different detection

architectures and devices have been investigated. Symons

et al. (2003) developed a machine vision based system to

classify durum wheat kernels according to the degree of

vitreousness. Wang et al. (2005) reached an overall correct

classification percentage of 94.83 % for vitreous, nonvit-

reous, and mottled (piebald) kernels using transmitted

images. Shahin et al. (2005), by processing of reflected and

transmitted images of vitreous kernels, achieved correct

classification percentages of about 91 and 87 % in the

training and test sets, respectively. Other advanced meth-

ods, including X-ray imaging (Haff and Slaughter 2004;

Karunakaran et al. 2003) and near-infrared spectroscopy,

have shown the potential for real-time application. Gordon


123


et al. (1997) used Fourier transform photo-acoustic infrared

spectroscopy to detect A. flavus in corn kernels. They

correctly classified all of the healthy and infected corn

kernels, although they used only a very small sample set

(20 kernels), they visually selected the top ten spectral

features, and they manually categorized the kernels into

classes based on spectral feature differences. For online

inspection, a large sample set, statistical computation, and

model development for future prediction are required and

should be validated. Color images of soybean seeds were

used to develop classification models to detect fungi-

damaged soybean seeds with classification accuracies of

77–91 % (Ahmad et al. 1999; Casady et al. 1992). Ridg-

way et al. (2002) used a monochrome CCD camera to

detect ergot in bulk wheat and rye samples and achieved a

detection rate of 87 %.

NIR-HSI and soft X-ray imaging for the detection

of storage fungi

As the NIR technique uses the spectral differences between

healthy and not-healthy kernels—which are caused by dif-

ferences in the chemical compositions of the healthy and

damaged kernels—for the discrimination, NIR-HSI tech-

niques represent an attractive solution for characterization,

classification, and quality control not only in the specific field

of wheat grains but also with reference to many different

materials/products found in several industrial sectors. Wang

et al. (2003) used near-infrared spectroscopy to develop a

two-class partial least squares (PLS) model and a five-class

artificial neural network (ANN) model to detect fungal

damage in soybean, and correctly classified 84–100 % of the

soybean seeds using them. Utilization of the visible

(490–750 nm) or NIR (750–1690) region alone led to a lower

classification accuracy, whereas combining the visible and

NIR regions (490–1690) gave higher accuracy. Singh et al.

(2010) used NIR hyperspectral imaging to detect fungal

infections of wheat, and developed statistical classifiers

[linear discriminant analysis (LDA), quadratic discriminant

analysis (QDA), and Mahalanobis]. A two-class LDA clas-

sifier correctly classified 95 % of the healthy kernels and

more than 96 % of the fungi-infected wheat kernels; how-

ever, their four-class model often misclassified kernels

infected with Aspergillus niger and A. glaucus. Serranti et al.

(2013) investigated the possibility of using hyperspectral

imaging techniques to classify different types of wheat ker-

nels: vitreous, yellow berry, and Fusarium-damaged. The

main problem encountered when developing sorting pro-

cesses based on optical sensors is the difficulty involved in

correctly classifying yellow berry and Fusarium-damaged

wheat kernels. Consequently, variable amounts of yellow

berry kernels are rejected along with Fusarium-damaged

ones, resulting in the rejection of some of the good kernels

and thus an economic loss. Reflectance spectra of selected

wheat kernels of the three types were acquired by a labora-

tory device equipped with an HSI system working in the

near-infrared field (1000–1700 nm) by Serranti et al. (2013).

The hypercubes were analyzed by applying different che-

mometric techniques, such as principal component analysis

(PCA) for explorative purposes, partial least squares dis-

criminant analysis (PLS-DA) for classification of the three

wheat types, and interval PLS-DA (iPLS-DA) for the

selection of a reduced set of effective wavelength intervals.

The study demonstrated that good classification results were

obtained when the entire investigated wavelength range was

considered, but also when only three of 121 narrow wave-

length intervals were explored (1209–1230 nm,

1489–1510 nm, and 1601–1622 nm).

The soft X-ray imaging technique has shown the

potential for application to fungal detection in corn

(Pearson and Wicklow 2006). As compared to conven-

tional methods, soft X-ray imaging is a simple, fast, and

nondestructive method. The soft X-ray technique has

already been investigated and has shown very high accu-

racy for the detection of sprout damage and vitreousness

(Neethirajan et al. 2007a, b) in wheat. Fungal infection of

grains leads to changes in the density of the grains. Such

density changes can be detected by comparing the features

extracted from the X-ray images of healthy and infected

kernels. The ability of X-ray imaging to detect fungal

damage in wheat and other cereals further increases the

versatility of the technique. Narvankar et al. (2009) soft

X-rayed healthy wheat kernels and kernels infected with

the common storage fungi (namely Aspergillus niger, A.

glaucus group, and Penicillium spp.), and algorithms were

developed to extract the image features and for classifica-

tion. A total of 34 image features were extracted and given

as input to statistical discriminant classifiers (linear, qua-

dratic, and Mahalanobis) and a BPNN classifier. A two-

class Mahalanobis discriminant classifier classified

92.2–98.9 % of the fungi-infected wheat kernels correctly.

The linear discriminant classifier gave better results than

the other statistical (quadratic and Mahalanobis) and the

neural network classifiers, identifying healthy kernels with

a classification accuracy of [82 %. In most cases, the

statistical classifiers gave better classification accuracies

and lower false-positive error rates than the BPNN

classifier.

Thus, NIR-HSI and soft X-ray imaging are potential

techniques for the determination of stored product insects

and fungi, and these methods need to be investigated fur-

ther for commercial applications.


123


Conclusion

The insect pests and fungi that infest stored products cause

huge capital losses due to quality deterioration and decreased

market values. Therefore, rapid and accurate methods for

their detection are vital, not only to facilitate their manage-

ment but also in trade. Among the various techniques used,

NIR-HSI and X-ray imaging have been identified as poten-

tial candidates for application to real-time situations and for

automation. These techniques have been demonstrated to be

very efficient for the detection of major stored grain pests,

such as S. oryzae, R. dominica, T. castaneum, and Asper-

gillus spp. Other than the very accurate detection of infes-

tations, these methods enable the detection of eggs and early

instars of internal grain feeders which are otherwise very

difficult to detect. The NIR-HSI-based approach is, however,

affected by drawbacks such as the need to develop robust

calibration models and to perform preliminary processing of

the collected data. That said, an imaging system that works in

the NIR possesses several advantages over X-ray imaging,

mainly in terms of cost, simplicity, compactness, and safety.

Both of these techniques are being developed for large-scale

automated detection at grain-handling facilities and quar-

antine centers.

Acknowledgments Figure 1 was reused from Kaliramesh et al.

(2013)), with permission from Elsevier Ltd. Figure 2 was reused from

Gowen et al. (2007), with permission from Elsevier Ltd. M.A. Shah is

also grateful to the University Grants Commission, New Delhi, for

granting a doctoral fellowship.

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