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Review
The biospeckle method for the investigation of agricultural
crops: A review
Artur Zdunek
n
, Anna Adamiak, Piotr M. Pieczywek, Andrzej Kurenda
Institute of Agrophysics, Polish Academy of Sciences, Doswiadczalna 4, 20-290 Lublin, Poland
article info
Article history:
Received 3 April 2013
Received in revised form
13 June 2013
Accepted 28 June 2013
Available online 23 July 2013
Keywords:
Biospeckle
Fruit
Quality
Nondestructive
abstract
Biospeckle is a nondestructive method for the evaluation of living objects. It has been applied to
medicine, agriculture and microbiology for monitoring processes related to the movement of material
particles. Recently, this method is extensively used for evaluation of quality of agricultural crops. In the
case of botanical materials, the sources of apparent biospeckle activity are the Brownian motions and
biological processes such as cyclosis, growth, transport, etc. Several different applications have been
shown to monitor aging and maturation of samples, organ development and the detection and
development of defects and diseases. This review will focus on three aspects: on the image analysis
and mathematical methods for biospeckle activity evaluation, on published applications to botanical
samples, with special attention to agricultural crops, and on interpretation of the phenomena from a
biological point of view.
&2013 Elsevier Ltd. All rights reserved.
Contents
1. Introduction: biospeckle phenomena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
2. Evaluation of biospeckle activity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
2.1. Global measures of speckle activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
2.1.1. Spatial and temporal contrast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
2.1.2. Time history of the speckle pattern (THSP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278
2.1.3. Spatial-temporal speckle correlation technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
2.2. Spatial analysis of biospeckle activity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
2.2.1. Fujii'smethod..........................................................................................279
2.2.2. Generalized difference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
2.2.3. Laser speckle contrast analysis (LASCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
2.2.4. Motion history image (MHI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
2.2.5. Analyses in spectral domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
3. Applications of the biospeckle-based methods in practical evaluation of agricultural crops. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
3.1. Monitoring of aging and shelf life of botanical samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
3.2. Relation of biospeckle activity with biochemical processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
3.3. Defects and diseases inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
3.4. Plant development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
4. Biological bases of the biospeckle phenomenon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
1. Introduction: biospeckle phenomena
The biospeckle phenomenon occurs when a biological material
is illuminated by a coherent light (laser light). The light is back-
scattered from an optically rough surface and due to interferation,
bright and dark areas appear on an observation plane (Fig. 1).
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/optlaseng
Optics and Lasers in Engineering
0143-8166/ $ - see front matter &2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.optlaseng.2013.06.017
n
Corresponding author. Tel.: +48 81 74 45061; fax: +48 81 7445067.
E-mail address: a.zdunek@ipan.lublin.pl (A. Zdunek).
Optics and Lasers in Engineering 52 (2014) 276–285
When the investigated object is static in time, the pattern is also
frozen. In the case of a biological material, first, light can usually
penetrate tissue and can be backscattered either from the surface
or by internal inhomogeneity. Second, the material, when living, is
not physically and chemically stable in time and space. This causes
the speckle pattern to also comprise a dynamic component. Draijer
et al. suggested that this dynamic behavior is mainly caused by
Doppler shifts of the light that interacts with the moving particles [1].
Particularly for biological media, the phenomenon of dynamic speckle
pattern has been called as the biospeckle.
The apparent activity of biospeckles is the result of physical
movement of particles inside cells and is affected by the variation
of the absorption of light by tissue pigments. Therefore, the
activity of biospeckles can provide information about various
living processes occurring inside a cell. Moreover, the method is
considered to be a nondestructive one because no visible results of
tissue interaction with light have been reported up to now when
low power lasers have been used. A typical setup for biospeckle
measurement is very simple (Fig. 2). It requires an expanded laser
light, which might be a diode laser, a detector such as a CCD
camera with a lens and a PC with a frame grabber that is able to
record a set of images with a constant time lag. From the time
series images, the biospeckle activity is estimated using various
mathematical methods. These features make this method attrac-
tive to many applications, which require fast and nondestructive
sampling.
This review consists of three main paragraphs presenting the
current stage in development of biospeckle technique and its
application, particularly for plant tissue evaluation. Since the
device is very simple and therefore most often is not a key issue,
first we will focus on the mathematical methods for measuring
biospeckle activity and will show different approaches, including
their pros and cons. Then the current stage in various applications
for botanical samples will be presented. The final section will
provide insight into interpretation of the phenomena for plant
tissue from a biological point of view.
2. Evaluation of biospeckle activity
2.1. Global measures of speckle activity
2.1.1. Spatial and temporal contrast
Assuming that the pattern produced by the light scattered by
the biological medium is composed of two components, one
produced by stationary scatterers and one by moving scatterers,
Briers concluded that, for long enough exposures or integration
times, the time-averaged speckle pattern of stationary scatterers is
constant at all points Briers [2]. Therefore, he expressed the
fluctuations of intensity of the speckle pattern as the ratio of the
mean intensity of the light from the moving scatterers to the total
intensity of the scattered light. This value, denoted by ρand given
in terms of the variance of the speckle patterns, is expressed by
the following equation:
ρ¼1s
〈I〉ð1Þ
where s
2
is the spatial variance of the time-averaged speckle
pattern and 〈I〉is the mean intensity of the pattern. This measure is
called the spatial contrast and can be calculated from a single-
exposure image speckle pattern. On images obtained using this
technique the areas with high speckle fluctuations are blurred
depending on the fluctuation velocity and therefore the spatial
contrast of these areas is reduced. In areas where there are no
fluctuations, the spatial contrast remains high [3]. Thus, the
integration time is the key factor in single-exposure photography
of biospeckle patterns and should be chosen depending on the rate
of observed fluctuations. Development of imaging techniques
using a CCD camera led to an alternative method of time averaging
where the ‘average’frame is calculated from a set of collected
frames. Moreover, it was shown that in the case of a set of sampled
frames it is possible to perform direct application of temporal
statistics, where the process of time averaging is replaced by
spatial averaging [4]. In this approach the first-order temporal
statistics of fluctuations in speckle intensity are calculated at each
point separately. This alternative measure, called temporal con-
trast, is expressed as:
ρ¼11〈s
2
t
ðx;yÞ
x;y
〉
〈I〉
2
"#
1=2
ð2Þ
where s
t2
(x,y) is the temporal variance of the intensity fluctua-
tions at point (x,y) of the speckle pattern and 〈…〉
x,y
indicates
spatial averaging. 〈I〉is the mean intensity of the pattern in the
temporal and spatial domain. Although spatial and temporal
contrasts are velocity-dependent values, they do not reflect
Fig. 1. A snapshot (a speckle pattern) from a movie of biospeckles recorded on an
intact apple. The speckle pattern is the result of interferation of backscattered
coherent light from the surface and internal structure. The biospeckles contain a
static component from non-moving tissue elements and a dynamic component
from non-static tissue elements. To watch the biospeckle movie we recommend
reference to Zdunek and Cybulska [17].
Fig. 2. Schematic view of the typical setup used for biospeckle measurement.
The system must contain a coherent laser light with optics to illuminate the object
surface evenly. A CCD camera is the typical detector used. The objective of the CCD
camera is usually strongly defocused. The aperture of the objective determines
speckle size daccording to formula d¼1.22(λz/D), where λis laser wavelength, zis
the distance of observation and Dis the diameter of the illuminated area. Larger
static speckles from the surface depend on the light incidence angle, whereas
smaller dynamic speckles resulting from interior of tissue are angle independent.
The final protocol for biospeckle measurement should consider laser wavelength,
the distance and angle between samples and the detector, polarization and light
intensity and the detector objective aperture.
A. Zdunek et al. / Optics and Lasers in Engineering 52 (2014) 276–285 277
information on the velocity of moving speckles or the frequency
components of intensity fluctuations. Instead, they estimate the
relative magnitude of speckle fluctuations [5]. Both methods use
first-order statistics of the speckle fluctuations, giving information
only about the relative contribution of moving scatterers to a
whole fluctuating speckle pattern [6].
2.1.2. Time history of the speckle pattern (THSP)
The THSP [7] is a matrix composed of a number of successive
images of dynamic biospeckle phenomena. The THSP is created
using the same column extracted from each image and placed side
by side. The set of columns is arranged sequentially in chronolo-
gical order. The width of the THSP is equal to the number of
images used and represents the time scale of biospeckle activity.
The THSP is composed of pixels in gray scale, arranged in
horizontal lines that show the time evolution of biospeckle
intensity fluctuations (Fig. 3a).
Arizaga et al. suggested an approach to characterize speckle time
evolution based on the inertia moment (IM) of the co-occurrence
matrix of the THSP [8]. The co-occurrence matrix [9,10] is built
from the THSP image and shows sample activity as a spread of
non-zero values outside the principal diagonal. For low activity,
the values of the matrix are concentrated around the diagonal,
while for high activity the matrix resembles a cloud. An image of
the co-occurrence matrix in false color-scale is shown in Fig. 3b.
The IM is defined as the sum of the matrix values times the
squared distance from the principal diagonal. It is expected that
a larger IM relates to higher biospeckle activity. Experiments with
a simulated THSP showed that the calculated IM decreases with
increasing length of the speckles and with speckle length above
one third of the window size the IM reaches its saturation state.
The inertia moment showed sensitivity mainly to the mean value
of speckle length. However, tests have indicated that the presence
of noise, which produces sudden jumps in intensity, might cause
interference and result in an increase in IM values [8,11].
The absolute value of differences (AVD) was proposed as an
alternative to the routine IM method [10]. Suspecting that the square
operation carried out in the IM method can emphasize biospeckle
activity changes in a distorted way, this new approach was based on
the calculation of the absolute value of the distance from the principal
diagonal. Analysis carried out by the AVD showed better results in
some cases of biospeckle activity, especially when the THSP matrix
contained no data at high frequencies.
Passoni et al. proposed wavelet entropy (WE) as the descriptor
of the dynamic speckle phenomenon [12]. Wavelet entropy was
used as a measure of order and disorder in a dynamical multi-
frequency signal of speckle activity. Each row of the THSP was
considered as an individual time series. A single image descriptor
was calculated as the mean value of WE from all rows of the THSP
image. WE showed better agreement with experimental data than
the inertia moment for changes in biospeckle pattern relating
Fig. 3. Two examples presenting low (a and b) and high (c and d) biospeckle activity analysed using time history speckle pattern (THSP) method. The THSP matrixes
presented in (a)–low biospeckle activity and (c)–high biospeckle activity has been constructed from 512 vertical lines recorded to show each pixel intensity history on the
horizontal axis. The pixels in the co-occurrence matrix (CO
ij
), presented in (b)–low biospeckle activity and (d)–high biospeckle activity, represent the number of occurrences
of pixel intensity jafter i. Since usually a CCD camera uses an 8-bit system, the CO matrix has dimensions of 256 256. A normalized inertia moment (IM) is calculated,
which is proportional to the spread of pixels in the CO matrix along its diagonal. A larger IM reflects higher biospeckle activity.
A. Zdunek et al. / Optics and Lasers in Engineering 52 (2014) 276–285278
to low frequencies [11,13–15]. This technique permitted both
qualitative and quantitative estimations, provided in shorter time
and requiring less data compared to other methods of THSP
analysis.
2.1.3. Spatial-temporal speckle correlation technique
This technique is based on the correlation analysis of two or
more speckle patterns, where one is considered as an image of a
reference state. The reference pattern and the patterns of subse-
quent object states are divided into an equal number of regions
and then cross-correlation of each pair of respective fragments is
calculated. The calculation of the cross-correlation coefficients for
a series of speckle pattern subimages recorded in the given
temporal order allows the temporal dependencies of these coeffi-
cients to be received as functions of the biospeckle pattern move-
ment speed [16]. Each such dependency is equivalent to temporal
degradation of a correlation peak. For a homogenous biospeckle
pattern the fluctuations of speckle intensity are equal in each
region, therefore the correlation peaks in all locations show similar
degrees of biospeckle pattern degradation in time. In this case a
grid of peaks can be replaced by one averaged value equal to the
value of correlation calculated between two images.
Biospeckle activity evolution can be evaluated by calculating
temporal changes in the correlation coefficient C
kτ
, where k is the
frame number and τis the lag time between frames (Fig. 4).
In practice, C
kτ
is calculated as the correlation coefficient of the
data matrix, consisting of intensities of pixels, of the first frame
with the data matrixes of the following frames of biospeckles.
Typically, the lag time is limited by the detector capabilities and
most often is about 1/15 s. A more pronounced decrease in C
kτ
(larger decorrelation) reflects higher biospeckle activity, therefore
the value 1C
kτ
is used for a more meaningful representation.
To simplify the analysis in practical application it is enough to take
just two snapshots of biospeckles with a lag of several seconds and
calculate the correlation coefficient between them [16–21]. This
“longer”lag time should be found for a certain system and a
material should be investigated as a compromise between the
time of measurement and the biospeckle decorrelation ratio.
2.2. Spatial analysis of biospeckle activity
2.2.1. Fujii's method
At first, Fujii's method [22,23] was applied in blood flow
observations using time varying laser speckles. This method is
based on calculations of weighted sums of the differences between
two consecutive elements of time sequence of intensities of each
pixel. The value of Fujii's index for a single pixel is defined as:
Fðx;yÞ¼ ∑
N
k¼1
jI
k
ðx;yÞI
kþ1
ðx;yÞj
I
k
ðx;yÞþI
kþ1
ðx;yÞð3Þ
where kis the image index from the sequence k¼1…Nand I
k
is
the intensity value of a pixel with xand ycoordinates. The image
composed of integrated F(x,y) values shows regions of high and
low speckle intensity with preservation of the contours of the
tested object (Fig. 5a). The process of weighting the differences by
the inverse of the sum of two subsequent intensity values results
in a nonlinear response that emphasizes both the large differences
as well as the small ones that involve values from the limits of the
dynamic range. However, Fujii's method has important drawbacks.
As can be seen, the same difference in intensity value can be dealt
with in completely different ways. For instance, the transition
from gray level 0 to 1 corresponds to the maximum value of Fujii's
index, while for a transition from 254 to 255 it takes the lowest
possible non-zero value. This might lead to the presence of false
activity in darker regions of the biospeckle activity map generated
by noise. A possible approach for overcoming these drawbacks is
to use the frequency decomposition carried out by the wavelet
transform [15,24,25].
2.2.2. Generalized difference
The Generalized Difference (GD) method was introduced as an
alternative to Fuji's approach [26]. In the basic version of this
algorithm the weighting process is eliminated. The GD is defined
as the cumulative sum of absolute values of the differences between
the pixel intensities among all frames and can be written as:
GDðx;yÞ¼ ∑
N1
k¼1
∑
N
l¼kþ1
I
k
ðx;yÞI
l
ðx;yÞð4Þ
where kand lare the frame indexes and I
k
is the intensity value of
a pixel with xand ycoordinates. Unlike Fujii's method, the
algorithm of GD includes the differences between nonconsecutive
frames. The minimum possible value of GD is obtained when there
are no differences between pixel intensities among all the exam-
ined time scales (or in other words, the differences are equal to
zero). When half of the pixel intensity values from sequence
k¼1…Nis equal to the upper bound of the dynamic range and
the second half possess the lower bound value, the calculated GD
reaches its maximum. However, due to the calculation of differ-
ences between all possible pairs of frames, the order of appearance
is not taken into account Therefore, this procedure generates a loss
of the temporal information of pixel intensity activity and shows
only the spread of values without any information about the
frequency of transitions. The contours of the examined object on
the resulting map of activity are less visible when compared to the
Fujii's method. The GD algorithm, when implemented directly, is
also more computationally time consuming. There is also other
variant of this method, where absolute values are replaced by
squared values. This measure, denoted by GD
n
,isdefined as
follows:
GD
n
ðx;yÞ¼ ∑
N1
k¼1
∑
N
l¼kþ1
ðI
k
ðx;yÞI
l
ðx;yÞÞ
2
ð5Þ
Fig. 4. Decrease of correlation coefficient C in time, calculated between the first
and consecutive frames of a biospeckle movie for a tomato. Biospeckle activity BA
can be calculated as 1-C at a chosen time of biospeckle evolution. For most fruits
there are 4 s of evolution, however this depends on laser quality (coherence).
For example, for a low cost diode laser, as was used by Zdunek and Herppich [18],
the evolution time was increased up to 14 s. This method is therefore very
convenient for future industrial application because it requires taking just two
snapshots with a lag of 4 s and allows determination of C between the two
matrixes.
A. Zdunek et al. / Optics and Lasers in Engineering 52 (2014) 276–285 279
These two approaches are not directly comparable, but have
qualitative similarities [27]. Images of biospeckle activity gener-
ated by the GD
n
method are characterized by a higher contrast
compared to GD. It has been shown that GD
n
has a straight
relationship with variance, therefore due to similarities between
GD and GD
n
there is also a qualitative relationship between GD and
variance. The loss of temporal data resulting from this fact was
eliminated in the WGD algorithm [26] by introducing to Eq. 4an
additional weight, which value varied along each summation
depending on time scale. The WGD is defined as:
GDðx;yÞ¼ ∑
N1
k¼1
∑
N
l¼kþ1
I
k
ðx;yÞI
l
ðx;yÞw
p
ð6Þ
where p¼lkand stands for the temporal distance from reference
frame k. The weight values vary with temporal distance p,
emphasizing fast or slow variations in speckle intensity.
The Fujii's and GD methods have one drawback in common that
the resulting matrix describes the speckle pattern activity as a
whole during the observations interval, but misses the evolution
of the activity. It is not the case in a similar method to them, a
temporal difference method which bases on subtraction of two
separated by a time interval speckle images [28]. This relatively
simple and modest computational costs method allows detection
whether the speckle structure has changed or not with addition of
obvious physical meaning of output pattern.
2.2.3. Laser speckle contrast analysis (LASCA)
LASCA is a technique that has been developed using a modified
Briers contrast method [2–4,29](Fig. 5d). Other terms used for this
method are “laser speckle contrast imaging”,or“laser speckle
imaging”[30–32]. In general, the LASCA algorithm is based on
calculations of spatial or temporal contrast over a local, square
window of MxMpixels. The window is moved along the speckle
pattern and the calculated contrast value is assigned to the central
pixel. The smaller the window, the lower the statistical validity,
while a larger window reduces the effective resolution of the
image. This method has many variations. In the most basic form
of LASCA, the contrast in each location is calculated from a
single image of the speckle pattern using a moving window. For
spatially derived contrast, LASCA is computed from set of collected
frames [33]. The calculations are performed for each frame
separately, as in the case of single-exposure photography. After
the contrast values are computed, they are averaged at each
location according to the present number of frames. The tempo-
rally derived contrast is calculated for each pixel separately using
data from all frames [30,34] and then smoothing by the moving
window is employed.
Biospeckle pattern mapping can be carried out using over-
lapping or contiguous windows. Using overlapping windows
results in smoother images, however as in case of contiguous
windows, the effective resolution of the final image is reduced by a
factor of M. Window size is adjusted according to the size of the
speckles. The most commonly used window sizes are 3 3, 5 5
or 7 7. Larger windows are not used due to too a high reduction
in image resolution. Due to the relatively fast computation
of LASCA, this method is applied for real-time monitoring. The
biggest drawback of LASCA in relation to other methods is the loss
of resolution [24,35]. Similarly to the Fujii's method, the
same range of fluctuations in intensity (expressed by variance or
Fig. 5. Real view (a), biospeckle image and (b)spatial biospeckle activity of the apple after local mechanical treatment in the center of apple, visualized by Fujii's (c) and
LASCA (d) method. This example shows that biospeckle imaging allows the distinction of intact and damaged areas (dark spot in the center of apple) even when fruit damage
is still externally invisible. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).
A. Zdunek et al. / Optics and Lasers in Engineering 52 (2014) 276–285280
standard deviation) will give a different contrast value in darker
regions than in more illuminated ones.
2.2.4. Motion history image (MHI)
An MHI is a real-time imaging technique that builds a map of
movement based on the recency of motion in an image sequence.
This technique was proposed as an alternative to the routine
online methods of biospeckle image analysis [35]. In an MHI, pixel
intensity is a function of the motion history at that location, where
brighter values correspond to more recent motion. The images
stored in a buffer are first processed. The processing procedure
consists of thresholding of the silhouette formed by the subtrac-
tion of two sequenced images. The final MHI image is created
using the procedure of weighting the threshold images stored in
the buffer, with respect to the “lifetime”of each image. Tested in
biological and non-biological samples, the MHI showed better
results when compared to the LASCA. Additionally, the MHI
provided similar results to the already known offline methods
such as Fujii's and GD.
2.2.5. Analyses in spectral domains
Due to the complexity of biological materials, alternative
techniques of biospeckle activity analysis, related on spectra
domains have been developed [5,15,25,36]. Transformation of
the biospeckle signal to the frequency domain can be performed
directly by the Fourier transform [36,37], or specifically by the
wavelet transform [5,15,25] which allows creation of new markers
related to physical or biochemical phenomena. The transformation
to spectra domain could be performed in conjunction with tradi-
tional biospeckle laser methods like Fujii, GD and THSP [15]. This
allows the spatial analysis of biospeckle activity with typical
indices, like IM, within chosen spectral bands. The spectral
characteristics of biospeckle activity showed promising ability to
distinguishing of processes related with water activity of maize
and beam seeds [15], separation of embryo and endosperm in
maize seeds [25] and observation, in frequency domain, aging of
apple, pear and tomato [38].
Using wavelet entropy as the descriptor of the dynamic speckle
phenomenon, as described in the section on global measures of
speckle activity, Passoni et al. showed a new method of evaluating
activity images [12]. The WE value was calculated over each time
series that corresponded to each image pixel, allowing the
identification of different activity level regions. The different pixel
gray levels of the resulting image are weighted according to the
WE values. The high activity regions (brighter areas) corresponded
to higher descriptor values, medium image values (middle gray
pixels) indicate lower activity and the lowest descriptor values
(darkest regions) correspond to the scene's inert background.
3. Applications of the biospeckle-based methods in practical
evaluation of agricultural crops
Since the biospeckle technique is non-invasive and nondes-
tructive to biological tissue, it is considered as a valuable tool for
monitoring various biological processes. It has been used for
example in medicine, most often for blood circulation measure-
ments [39–42] or in microbiology for bacterial (Pseudomonas
aeruginosa) motility assessment [43]. For agricultural crops, which
are the topic of this review, various studies were carried out to
show the usefulness of the method for monitoring aging (or shelf
life), detection of defects and diseases, evaluation of stages of plant
development and in general to understand the phenomenon from
the biochemical point of view.
3.1. Monitoring of aging and shelf life of botanical samples
In the case of plant tissue, biospeckle fluctuations are usually
not a result of well-defined fluxes of matter, such as those
observed during blood flow. Therefore these case studies were
carried out to show the correlation between biospeckle activity
and quality indices or physiological processes. The first work in
this area was presented by Oulamara et al. [7], who showed
differences in temporal speckle evaluation among different com-
modities, i.e., an apple, an orange and a tomato. Xu et al. for the
first time showed that biospeckle could be used for monitoring the
age and shelf life of botanical specimens (oranges, potatoes,
apples, radishes, tomatoes) because the temporal fluctuations of
speckles decrease with aging [44]. In this experiment biospeckles
were recorded in free-space propagation geometry, using a polar-
ized He–Ne laser as a source of coherent light and a CCD array
camera interfaced to an image processor. The results were con-
firmed by Zhao et al. for an apple, a tomato and soybean sprouts
and leafs [45]. Aging and decrease in vitality causes a decrease in
biospeckle activity. This paper also reported that higher biospeckle
activity indicates higher flow rate in the veins of botanical
samples, thus this method can also be used for monitoring
nutrient transportation. Zhao et al. [45] recorded biospeckles by
the point-wise and whole-field methods using different system
arrangements. Both systems also used a low power He–Ne laser
with a wavelength of 633 nm. In the case of the whole-field
method, the laser beam was expanded and the speckle intensity
was recorded as a function of time at three arbitrary pixels. Auto-
correlation coefficients, the cut-off frequency of the biospeckle
temporal spectrum and the contrast were calculated as the
parameters characterizing activity.
A typical shelf life experiment for apples showed a very
pronounced decrease in biospeckle activity with days of storage
[16] and that the activity correlates very well with the firmness of
fruits [46]. Rabelo et al. have monitored the senescence process of
an orange in the postharvest time and in this case also the
biospeckle followed the biological variations [47]. Two applied
approaches for quantification of the temporal biospeckle fluctua-
tion (the inertia moment of the co-occurrence matrix and the
statistical cumulant of the auto-correlation function of the spatial
temporal speckle pattern STS) showed a decrease in bioactivity
during orange storage. Moreover, tested fruits exhibited different
values of the measured speckle pattern dynamics, depending on
the illuminated point. Authors concluded that calculation of the
activity obtained for the central calomel base (apex) allowed
differentiation between oranges according to their freshness.
Obtained results suggested that biospeckle might be used in
packing houses as an alternative quality evaluation tool.
3.2. Relation of biospeckle activity with biochemical processes
Aging and the shelf life of botanical samples are accompanied
by various biochemical processes. Therefore, observed biospeckle
activity could reflect the dynamics of metabolic-related changes in
tissue. However, interpretation of biospeckle activity from this
point of view is very scarce. Few studies have shown that
biospeckle activity depends significantly on biochemical changes
occurring during the postharvest storage of apples. Starch granule
degradation caused a lesser number of scattering centers in the
apple tissue and resulted in smaller biospeckle activity, evaluated
on the basis of cross-correlation calculation [17]. It has also been
proven that storage temperature affects measured biospeckle
fluctuation [19]; calculated indices of apple biospeckle dynamics
(i.e., speckle contrast, moment of inertia and correlation coeffi-
cient) decreased when the sample was cooled down. In other
words, freezing the tissue causes biospeckle activity to stop. In the
A. Zdunek et al. / Optics and Lasers in Engineering 52 (2014) 276–285 281
above studies similar experimental devices were used. A low
power red laser illuminated the apple surface and biospeckle
activity was recorded with a CCD camera with a strongly defo-
cused objective. The angle of incidence of the laser beam on the
botanical specimen was equal to 301and the obtained average
speckle size was adjusted to make it much larger than the pixel
size of the camera. Influence of chlorophyll content on measured
apple biospeckle activity was investigated by Zdunek and Herp-
pich [18]. They stated that biospeckle activity linearly decreased
with an increase in chlorophyll content, which could be related to
chlorophyll red light absorption. This effect limits the amount of
light that can penetrate the tissue. This result confirmed the
previous results of Romero et al. [48] in tomato ripening degree
determination.
3.3. Defects and diseases inspection
The presence of surface and under surface defects alters
biological activity and can affect observed biospeckle variations.
Pajuelo et al. tested damage in apples by using several dynamic
speckle techniques [9]. They used quantitative methods, including
autocorrelation of the temporal history of speckle patterns. The
moment of inertia of the co-occurrence matrix and the statistical
cumulants calculation showed consistent results; after a mechan-
ical injury biospeckle variations were lower than before and these
fluctuations decreased with time. To display the activity in a
qualitative way and to visualize the damaged zones, the weighted
generalized difference (WGD), the LASCA and the Konishi method
were used. These techniques showed similar results; the bruised
region could be distinguished from the undamaged surrounding
areas. Later Passoni et al. analysed bruising in apples by means of
entropy wavelet methods and they also obtained satisfactory
results, allowing for quantitative and qualitative estimation of
the sample bioactivity [12].
The possibility of biospeckle technique application for monitor-
ing apple fungal infection development has been recently reported
by Adamiak et al. [20]. An experimental device was equipped with
a laser (8 mW, 635 nm) with an expanded beam, which illumi-
nated apples at six places along the equator. A CCD camera
recorded 4 s films to calculate the correlation coefficient. 100
apples were monitored during cold storage for a long period with
special attention to the development of bull's eye rot. Three stages
of biospeckle activity were observed (Fig. 6): the first one was
when biospeckle activity decreased due to shelf life-like behavior
(as described previously), the second stage was when biospeckle
activity increased significantly due to first symptoms of bull'seye
rot infection and the third stage was when biospeckle activity
suddenly decreased due to extensive rotting of the tissue. The
increase in biospeckle activity due to infection was also observed
on apparently healthy parts of the fruit. Precise monitoring of the
infected part of the apple tissue proved that early infection caused
an increase in biospeckle activity, which can be used for non-
destructive detection of apple diseases.
Biospeckles were also implemented for the detection of fungal
contamination in seeds [36,49]. Braga Jr. et al. adopted the GD and
IM methods, as well as Fujii's algorithm, for evaluation of the
biospeckle fluctuation in beans'seed [49]. Obtained results
showed the capability of the employed technique for the identi-
fication of microorganisms'presence in beans. Seeds inoculated
with fungi exhibited greater values of IM (higher biospeckle
activity) compared to the control group. In addition, two of the
three used fungi species could be distinguished by a distinct range
of IM values. The GD and Fujii's method image techniques also
displayed the presence of fungi. Rabelo et al. [36] analysed the
registered biospeckle images of bean seeds, artificially contami-
nated by fungi, using an inertia moment method and the
frequency values of the STS signals, derived using a fast Fourier
transform (FFT). The authors obtained similar results as Braga Jr.
et al. [49]; intensities of IM values of inoculated seeds were greater
than those of disease-free seeds. FFT coefficients also showed
differences between contaminated and healthy beans. However,
the authors concluded that IM values allowed the distinction
between different fungi species, whereas frequency analysis was
useful in this matter only within certain harmonics.
3.4. Plant development
Information on the application of biospeckle for monitoring
plant development is scarce.
Briers suggested the use of laser speckle fluctuations as a
screening test in assessing the suitability of specimens for holo-
graphic measurement of growth or motion [50]. Braga et al.
evaluated ability of this technique to detect changes in biological
activity across root tissues [24]. Roots of Coffea arabica and
Eucalyptus grandis plants were grown in gel tubes and were
illuminated by an expanded laser beam (10 mW He–Ne laser,
632 nm) using a forward and backscattering configuration. Images
were collected by CCD cameras and analysed by generalized
difference GD, Fujii and LASCA methods. These approaches were
assessed visually by their ability to discriminate between different
regions of the roots. The most satisfactory maps of biological
activity in root tissues were obtained using the GD method. The
results showed that biospeckle could be a quantitative indicator of
root cells molecular activity.
Recently, Szymanska-Chargot et al. monitored apple develop-
ment with the use of a biospeckle technique [21]. Biospeckle
activity increased during pre-harvest apple development. A sig-
nificant correlation was obtained between measured biospeckle
activity and soluble solid content, starch content and firmness,
showing that this method has the potential to be used for non-
destructive evaluation of these properties in the pre-harvest
period. The device for dynamic speckle pattern acquisition con-
tained an expanded diode laser beam (8 mW, λ¼635 nm) and a
CCD camera to record AVI films of biospeckle fluctuation. Bios-
peckle activity (BA) was evaluated based on a correlation coeffi-
cient calculation.
4. Biological bases of the biospeckle phenomenon
As was summarized by Aizu and Asakura [51], specific char-
acteristics of biospeckles are: multiple scattering of light in living
Fig. 6. Bull's eye rot development monitored with biospeckle activity BA during
cold storage of apples for 140 days. This is schematic profile based on monitoring
100 apples at 6 spots on the equator. The significance of the changes was confirmed
by ANOVA.
A. Zdunek et al. / Optics and Lasers in Engineering 52 (2014) 276–285282
tissue, different dynamics of speckles observed on one specimen
and dependence of dynamics on the structure and activity of
objects, which may carry useful information about the biological
and physiological activity of living objects. Statistical analysis of
the biospeckle phenomenon can therefore be applied not only to
non-contact measuring of specific characteristics of living speci-
mens but also, regarding the sensitivity of organisms to environ-
mental conditions, in the bioindication of the environment.
According to the type of tested organism, the processes under-
lying the phenomenon may be different. In the case of small
moving organisms and independent, individual cells moving
passively (e.g., blood cells), biospeckle dynamics is caused mainly
due to light scattering on the moving objects, while the dynamics
observed in stable cell colonies or tissues is mainly the result of
cellular transport, organelles movement and Brownian motion and
diffusion inside the objects [7]. In practice, a biospeckle image is a
mixture of speckles with different fluctuations of intensity
depending on its origin. As a fast component of biospeckle
dynamics, random physical movements are considered, while a
slow component could be caused mainly by the movement of a
biological origin. The biological source of biospeckles was con-
firmed in experiments showing their dependence on temperature
[19], the wavelength of the light used, the color of the object [52]
and light intensity [7].
Taking into account attempts at the practical application of
biospeckle, the most numerous and most advanced studies have
been conducted in the medical field. Applications in the field of
medicine, comprehensively described in [53], include blood flow
velocity measurements in different organs [42,54–56] as well as
muscular activity [57]. Moreover, biospeckle technique has been
proven to be a valuable tool for cancer tissue detection [25,58].
Less numerous but more diverse experiments have been carried
out in other areas of biology and agriculture, which relate
primarily to the two groups of research: measurements of motility
of microorganisms and the evaluation of different properties of
plants. The main principle of the application of biospeckle in
determining microorganism motility is measuring the dynamics of
interference pattern formed by the scattering of light on the
moving objects. The faster the microorganism motility, the higher
biospeckle activity is. This method was used in the detection
of fungi [49] and the viability of gametes [59], crustaceans
[60], parasites [61] and microorganisms in liquid culture media
[62,63].
Interpretation of biospeckle dynamics in the case of their
application to plants is much more difficult. Activity of interfer-
ence pattern observed on plants is an effect of the changes of
optical properties of cell compartments, physical and biological
mechanisms connected with movement inside the objects and
changes in plant structure due to growth or turgor regulation.
Optical properties, like the number of scattering and absorbing
elements and the high variability of densities in intracellular
compartments, affect the path of photons and change the number
of potential interactions between light and matter, while the
movements of tissue elements of physical and biological origin,
increase the dynamics of the biospeckle image. Thus, the bios-
peckle can carry information about both, the structure and
biological activity of the object. However, this information is
encoded in the complex spatio-temporal changes of the interfer-
ence pattern.
Experiments with the use of biospeckle as a tool for studying
the structure of plants, refers mainly to the measurement of
growth and movement in plant organs [51,64]. However, most
researchers attempt to apply biospeckle in monitoring the proper-
ties of plants connected with internal physiological processes.
In early work by Briers, one can find information that biospeckle
dynamics arise mainly due to chloroplast movements, since the
size of these objects according to scattering theory is optimal as a
scattering centers and corresponds to the wavelength of laser light
used [52]. However, biospeckle could also arise from the scattering
of light on other organelles, macromolecules and inorganic particles
with sizes in the scale of nano- and micrometers. This observation is
supported by the studies of Zdunek and Herppich [18] and Cybulska
and Zdunek [17], who showed a correlation between starch and
chlorophyll content in apples and biospeckle activity. An increase in
biospeckle activity with a decrease of chlorophyll content was
caused by a decrease in red laser light absorption, deeper penetra-
tion of the apple tissue by light, multiplication of internal reflections
on moving cellular elements and finally, an increase in the
dynamics of the interference image. In turn, an increase of bios-
peckle dynamics with an increase in starch content increases the
number of moving scattering centers in the tissue and in conse-
quence biospeckle activity. Further examples of the application of
biospeckle in the evaluation of fruit and vegetable quality have been
presented in a number of publications [16,46,48,65,66]. The exis-
tence of a relationship between biospeckle activity and qualitative
parameters in fruits and vegetables (which are, in fact, a group of
physical and chemical properties of plant tissues dependent on
plant metabolism) during storage or ripening also suggest a
biological origin of this phenomenon.
According to the assumption that biospeckle is the result of
movement inside living organisms and that this movement has a
mainly biological character (excluding Brownian motion and
diffusion), several of the most likely, extra- and intracellular
sources of light scattering, dependent on plant metabolism can
be identified.
Long distance water transport velocity in vascular bundles can
reach values up to few millimeters per second [67]. Peak flow rates
in xylem are about 1mm/s, although maximum velocities as high
as 0.8m/s have been reported [68]. Therefore, water transport in
xylem vessels can cause vibration in the conductive elements and
surrounding tissues because of cavitation [69–71], which might be
the source of biospeckle dynamics.
Another potential process, which could be the basis of bios-
peckle, is cytoplasmic streaming. This directed flow of cytosol and
organelles can reach even up to 100 mm/s [72]. Organelles, as well
as dissolved macromolecules moving with the flow, are scattering
centers, which can cause dynamic light scattering. Cytoplasmic
streaming and other processes connected with intercellular move-
ment occur as a result of the functioning of motor proteins.
A mechanism based on actin and myosin is mainly responsible
for the formation of cytoplasmic streaming and division of the cell
while microtubules with miosins and kinesins provides vesicle
transport and cell spatial reorganization. Motor proteins are there-
fore potentially the most important, but not the only source of
biospeckle in cellular scale.
Ion channels and proton pumps also should be included as
potential cellular elements that may affect biospeckle activity.
Regulation of cell turgor and changes in the volume of specific
intracellular compartments is a direct result of channel and ion
pumps functioning and can also result in dynamic light scattering.
Extracellular processes that are closely related to the metabo-
lism of cells connected with cell wall growth and degradation can
be source of biospeckle as well. Excretion of cellulolytic enzymes
relaxing the cell wall and increase of turgor pressure during
growth, according to acid growth theory [73,74] can cause the
displacement of cell wall material, which can be the direct source
of dynamic light scattering. On the other side, chemical changes
accompanying cell wall degradation during aging or damage,
leading to changes in the spectral properties of the tissue in the
direction of greater light absorption, could decrease the amount of
diffusively reflected light and in consequence decrease biospeckle
dynamics.
A. Zdunek et al. / Optics and Lasers in Engineering 52 (2014) 276–285 283
On the basis of the presented theoretical information one can
conclude that in organs or parts of organs with high metabolism
rate e.g. during growth or turgor regulation, biospeckle activity is
relatively high, while in organs with a high pigment content or in
the final stages of development biospeckle activity is relatively
low. This observation allows for a theoretical indication of the
potential practical applications of biospeckle. In the case of fruits
or parts of plants consisting mostly of parenchyma, biospeckle
activity probably reflects the general rate the sum of metabolic
processes, which allows for an overall assessment of the physio-
logical state of these objects and identification of areas with
different biospeckle activities as a result of damage or pathogen
attack. However, when the part of the plant consists of a variety of
tissues, it may also be possible to assess the differences in activity
between the tissues in which the sources of biospeckle are
different or the rate of the processes in tissues is variable. An
example of a potential application of biospeckle is the study of leaf
physiology, where, completely non-destructively, changes in the
rate of water transport in vascular bundles or changes of turgor in
stomatal cells or pulvini should be visible.
However, the use of biospeckle entails a number of problems,
among which the most important are: determination of the
quantitative relationship between the rate of a biological process
and biospeckle activity and the distinction between levels of
activity for individual biological processes and the sum of all the
processes that are sources of dynamic light scattering in the object
[75]. The solution to both problems may be the development of
new measurement methods with the use of frequency analysis
and basic research concerning the relationship between specific
biological processes and biospeckle activity.
5. Conclusions
Several reports about the use of biospeckle technique in the
analysis of agricultural crops are presented in this review. Experi-
ments showed that biospeckle activity correlates with physiologi-
cal changes during development and ripening and with defects
and diseases. From this point of view one can state that the
method has important potential to be used as a nondestructive
method of quality control or as an indicator of maturity state.
However, an important obstacle for agriculture industry level
application is a lack of good calibration or classification models.
The reason for this is two-fold. First, the origin of biospeckle
activity is a very complicated interaction between light and a
botanical sample, where both elastic and non-elastic scattering
occurs, additionally affected by biochemical absorption. This is the
case of course for other optical methods, however biospeckles
have the additional factor of physical particle movement, which is
difficult to quantify for the preparation of calibration models.
Actually, the sensitivity for particle movement makes this method
particularly suitable for monitoring processes related, for example,
with water movement in vessels, which occurs relatively faster
than processes like pigment or polysaccharide degradation and
therefore is not affected by these biochemical changes. A second
problem is sensitivity to external vibrations. Most of the setup and
methods of data analysis need,many images to evaluate biospeckle
activity. When some vibration in the system occurs a large
decorrelation is detected.
The most important advantage of this method is its nondes-
tructive character and the fact that it involves an uncomplicated
and flexible system. A low power laser (a few mW), which very
often is expanded from a diameter of 1 mm to several centi-
metres, cannot usually heat large samples such as fruit, for
example. The configuration laser-object-detector can be setup
horizontally or vertically and its simplicity makes it relatively
easy to achieve connection to prevent the effects of vibrations.
In addition, new cheaper system based on optical mouse are
introduced [76].
Both, laboratory and industry applications require very accurate
interpretation of biospeckle phenomena. Former studies consid-
ered this issue in a very general way, i.e., they interpreted
biospeckle activity as cyclosis and Brownian motion movement
affected by pigment absorption. Biological samples are very vari-
able and undergo very pronounced pre- and postharvest changes,
for example starch and chlorophyll degradation or changes in
water content and surface roughness. Therefore, the question
which quality-related processes are responsible for the change in
biospeckle activity is very difficult. To find the answer there are
two ways: first, by working on model or simplified systems and
second to improve the frequency analysis of biospeckle activity to
deconvolute signals for distinct biological processes.
In summary, the biospeckle method shows plenty of interesting
nondestructive applications in agricultural crops. At present, in the
area of quality evaluation, this method is still under development
and has a chance to be commercially used as it is already utilized
in medicine. However, industry scale application for agricultural
crops requires more specific studies to obtain calibration or
classifications models dedicated to a certain commodity.
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