Evaluation of the storability of Piel de Sapo melons with sensor fusion
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Information and Technology for Sustainable Fruit and Vegetable Production
FRUTIC 05, 12 – 16 September 2005, Montpellier France
Evaluation of the storability of Piel de Sapo melons with sensor fusion
L. Lleó, P. Barreiro, A. Fernández, M. Bringas , B. Diezma and M. Ruiz-Altisent
Physical Properties Laboratory, E.T.S.I.A., Polytechnic University of Madrid, Avda.
Complutense s/n, 28040 Madrid, Spain, Tel: +34 913 365 862 Fax: +34 913 365 845
pbarreiro@iru.etsia.upm.es
Abstract
Several varieties of melon have been evaluated under their storability point of
view. Destructive (hollow volume, soluble solids, Magness-Taylor firmness) and non
destructive measurements (impact firmness, acoustic response, multispectral
features) have been carried out. Acoustic response shows a main variance in the
range of 78-225 Hz, decreasing when hollow volume and maturity increase.
Multispectral images in chlorophyll band was selected as a suitable complement to
acoustic frequency. Non supervised classification at harvest with multispectral
camera is strongly correlated with acoustic frequency and impact acceleration.
Fusion of acoustic response and multispectral classification allows to differentiate
between internal hollows and maturity.
Keywords: Melons, acoustic response, multispectral, sensor fusion, firmness, internal
hollow, maturity
INTRODUCTION
Non destructive techniques exploiting the sonic characteristics of fruit tissue have
been applied for firmness measurements as well as to internal disorders in several
products such as apples, pears, avocados and melons. Frequently, instruments deliver an
impulse to the fruit to produce acoustic vibration (Farabee and Stone, 1991; Armstrong et
al., 1997; De Belie et al, 2000). Different systems are used to sense the vibration of a
fruit. Some instruments have piezo-electric sensors, while others employ microphones
(De Baerdemaeker et al., 1982; Armstrong et al., 1990; Stone et al., 1996; De Belie, et
al., 2000; Diezma et al. 2003).
Based on instrumental measurements, as well as on theoretical analysis, two
fundamental mode shapes referred to as torsion modes and spherical modes have been
found to exist for different fruits. Only some resonant frequency modes shapes has been
related to fruit firmness. The experimental setup used in this research provides the
resonant frequency of one spherical mode. It was reported by Stone et al. (1996) that in
‘Galia’ melon the first-type spherical resonant frequency measured around the equator
was between 203 and 208 Hz, which agrees well with the values of the peaks considered
in our research. Former relations between this resonant frequency and melons firmness
were established (Stone et al. 1996).
Multispectral imaging may be used to address external features such as ripening
(Lu, 2004) and external defects with higher sensitivity compared to ordinary RGB
imaging (Aleixos et al. 2002, Leemans et al., 2002, Kleynen et al., 2004). The spectral
bands used for this study were selected in a previous research works (Ruiz-Altisent et al.,
2000).
The objective of this study is fuse the acoustic impulse response and multispectral
images in order to predict the storability of individual piel de sapo melons within an
online prospective.
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SensorsInformation and Technology for Sustainable Fruit and Vegetable Production
FRUTIC 05, 12 – 16 September 2005, Montpellier France
MATERIALS AND METHODS
Several varieties of melons Piel de Sapo (‘Abran’, ‘Pinzón’, ‘MP-899’, ‘Seda’,
‘Nicolás’, ‘Valverde’, ‘Babiera’, ‘MP-857’, ‘MP-907’, ‘MP-910’, ‘Cantasapo’, ‘Ruidera’,
‘Trujillo’, ‘Montijo’), have been evaluated under their storability point of view with
destructive and non destructive techniques. Two main experiments have been carried out.
The first one aimed to address the maturity variability and internal quality at harvest and
consisted of analyzing 135 melons evaluated for color, external hardness (impact),
internal texture, hollow volume (missquality factor) and soluble solids (ºBrix) as
reference parameters, while acoustic impulse response and multispectral images were
used as non destructive procedures under sensor fusion strategy. The camera employed
was a 3 CCD RGB (Red, Green and Blue) camera; each channel was centered in a
specific wavelength: 660, 540, 460 nm respectively with a 40 nm of bandwidth in all
cases.
The second experiment (500 melons) was designed to evaluate mentioned non destructive
techniques when used to predict the potential storability of melons. Melons were analyzed
with mentioned non-destructive procedures at harvest and with both destructive and non
destructive methods after one month storage at 20ºC. Also a set of 60 melons was
analyzed with non destructive techniques four times along storage. In this case an
Infrarred (IR), Red (R), Blue (B) camera was employed. (IR= 800 ±20nm, R=675±20nm,
B=450±20nm). In this experiment, experts evaluated the melons and gave them a
maturity score from 1, ‘under ripe’ to 5 ‘over ripe’.
RESULTS
Acoustic response and internal hollow relationship
Acoustic impulse response shows a main variance area in the range of 78-225 Hz (see
figure 2) which corresponds to the second vibration frequency. This vibration mode
correlates with the hollow volume but also to over-ripening, (see figure 1 on the right).
Multispectral images was selected as a suitable complement to address whether a
frequency decrease is due to over-ripening or to internal hollow.
600
500
400
Hollow volume (ml)
300
200
100
0
80 100 120 140 160 1 80 200 220 240 260 280 300
average peak f req. (Hz)
Figure 1. Variability of internal hollow (left) and correlation between hollow volume and 2nd
vibration frequency (right) for the 150 melons corresponding to the initial experiment
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FRUTIC 05, 12 – 16 September 2005, Montpellier France
Figure 2. Visualization of covariance matrix of acoustic spectra (Hz) considering the set
of 60 melons along storage period (one month). The negative value of covariance is due
to the displacement of the vibratory frequency to the left (lower value) as the period of
storage increases. See also next figure
1500 1500
amplitud (verde=dia0, azul=dia1, rojo=dia3, negro=dia4 )
amplitud (verde=dia0, azul=dia1, rojo=dia3, negro=dia4 )
1000 1000
500 500
0
100 150 200 250 300 0
100 150 200 250 300
frecuencia Hz
frecuencia Hz
Hz Hz
Figure 3. Second vibration frequency (x-axis). Amplitude (y-axis) considering storage period;
green: no storage, blue: two weeks, red: three weeks, black: four weeks. Post harvest evolution of
two melons. Left melon was classified as storable though clear postharvest ripening is found.
Right melon was classified as not storable and after 2 weeks of storage it had to be rejected due
to unmarketable conditions.
RGB and IR, R, B cameras
1. Maturity estimation
The best relation between RGB images and expert evaluation was found in R channel. R
histograms present a displacement to higher grey level values as the maturity score
increases. At harvest, riper fruits reflect a higher amount of Red light while unripe ones
are darker in that band, as expected for higher chlorophyll content.
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SensorsInformation and Technology for Sustainable Fruit and Vegetable Production
FRUTIC 05, 12 – 16 September 2005, Montpellier France
RGB Camera.
R channel (660 ± 40 nm) in this camera has wider wavelength range than R (675 ± 20
nm) for the IR, R, B camera. In both cases detector includes the chlorophyll peak
absorption 675 nm.
A non supervised classification based on Ward method is employed using all grey level
from 30 to 240, which correspond to fruit segmentation thresholds compared to the
background. Higher values than 240 correspond to very yellow coloured areas. Lower
values than 30 are nearly constant. Two classifications were made independently for the
bed side of images, and for the opposite side. The best results were obtained
corresponding to bed images. Six natural grouped clusters were found in the population at
harvest (135 melons from experiment 1). Three of them correspond to small fruits and the
other three to large size melons, according to the size camera estimation (sum of pixels
belonging to the fruit). In both size clusters, three maturity levels are found (fig 4).
clu1-23 clu2-15 clu4-26 clu3-29 clu5-22 clu6-20
12000 7000
6000
9000 5000
4000
6000
3000
2000
3000
1000
0 0
R15 R45 R75 R105 R135 R165 R195 R225 R15 R45 R75 R105 R135 R165 R195 R225
Figure 4. Mean histogram for each non supervised category. On the left, big size groups: cluster
2 unripe, 1 medium, 4 ripe. On the right, small size clusters, 6 unripe, 5 medium, 3 ripe. The
histograms move to the right when maturity increases. The number of fruits inside each cluster
is indicated.
Some relationships are found between the RGB classification and the acoustic response.
When use resonant 200 Hz as threshold, fifty percent of big melons classified as non
mature with the RGB camera show higher frequency. For medium, 25% of melons are
above 200 Hz and only 10% for the ripest cluster. No tendency is observed in small size
groups
IR, R, B camera.
Channel R 675 ± 20 nm, is narrower than R channel from R, G, B camera and we expect
the images to be more related to chlorophyll degradation, and therefore to maturity. The
described non supervised classification method was also applied. The grey level
considered were from 15 to 150 which correspond to melon surface excluding lightest
areas. Five categories from unripe to over ripe were found at harvest (190 melons) within
storage experiment (500 melons in total). Again, the average histograms move towards
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SensorsInformation and Technology for Sustainable Fruit and Vegetable Production
FRUTIC 05, 12 – 16 September 2005, Montpellier France
higher values. As cluster number increases, two regions seem to appear in the histogram.
Two different populations appear inside the same image, inside the same fruit, probably
corresponding to differences in chlorophyll content. Possibly due to a non homogeneous
maturity process.
30000
clu1-33 clu2-47 clu3-31 clu4-22 clu5-57
25000
20000
15000
10000
5000
0
R15 R45 R75 R105 R135
Figure 6. Mean Red cluster histogram from 1, unripe to 5, over ripe. Bed images, 190
fruits. Red channel from IR, R, B camera.
Experts score and camera classification present the same tendency. All fruits belonging to
high categories (experts scores 4 and 5; clusters 4 and 5) present low firmness values.
Comparing firmness (impact acceleration) with non supervised bed image classification, a
clear tendency is found (see figure 6).
Figure 6: Acoustic frequency (x-axis), maximum impact acceleration (m/s2), and camera
classification (no bed data, 190 fruits). Each point represents one fruit. As cluster score
increases, the distribution of melons moves from high firmness (more than 700 m/s2),
high acoustic (above 200 Hz) to lower values. High maturity multiespectral
classification (clusters 4 and 5) presents the whole range of frequency and lower
firmness values
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SensorsInformation and Technology for Sustainable Fruit and Vegetable Production
FRUTIC 05, 12 – 16 September 2005, Montpellier France
Figure 7. Acoustic frequency (x-axis), hollow volume (y axis), for each non supervised
classification score. Cluster 1 presents the highest frequency and cluster 5 the lowest.
Hollow volume is independent from multiespectral classification, and is negatively
correlated with frequency. Camera classification is also negative correlated with
frequency; riper fruits move to lower frequency. This relation is clearer than for the RGB
camera. All fruits from cluster 1 present high frequency response. Few cases (3) in
cluster 5 present frequency higher than 200 Hz.
2. Feature selection from histograms. Discriminant analysis.
In both cameras the aim was to select the most discriminate variables, extracted from the
Red histograms, to separate as much as possible each cluster from the others.
Forward stepwise analysis was applied within this aim using cluster number as dependent
variable. The independent variables, in the case of RGB camera, were grey levels from 40
to 140. Grey levels from 15 to 150 for IR, R, B. For the RGB camera the variables
selected by mentioned procedure were level 60 (the most discriminative), 105 and 200.
Considering only the first two, the percentage of correct classification was 91, 9 %. The
IR, R, B camera was better as maturity classifier. In this camera, five maturity levels, and
not only three could be segregated. Using 78 and 105 grey level the percentage of correct
classification is 82,6%. When grouping fruits into three classes, the percentage of correct
classification was 97,4 %. Figure 7, shows cluster classification with IR, R, B camera
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SensorsInformation and Technology for Sustainable Fruit and Vegetable Production
FRUTIC 05, 12 – 16 September 2005, Montpellier France
clu1 clu2 clu3 clu4 clu5 f1yf2
f1yf3 f2yf3 f2yf4 f2yf5 f4yf5 f3yf5
12000
10000
8000
90,3%
r105
6000
96,5 %
4000
61 %
2000
81 % 73 %
0
0 5000 10000 15000 20000
r78
Figure 7. Representation of IR, R, B clusters and boundaries of discriminate functions
(f1 to f5) with r78 and r105 (190 fruits). The maximum overlapping occurs between
classes 1 and 2. Extreme clusters are completely separated one another. Arrows
indicate the maturity evolution.
CONCLUSIONS
Maximum acoustic variance is found in the range of 78-225 Hz. The second vibratory
frequency correlates negatively with hollow volume and maturity.
At harvest Red histograms present a displacement to the right as maturity increases.
Camera non supervised classification is strongly correlated with frequency and firmness
impact aceleration. This tendency is clearer in IR,R,B camera than in RGB.
Expert and camera maturity classifications are correlated.
Fusion of acoustic response allow to address whether frequency decrease, due to internal
hollow or/and over ripening.
CITATIONS
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vibrations in apples for firmness determination. Transactions of the ASAE. 33: 1353-
1359.
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peaches: Optical and mechanical parameters. Proc. AgEng2000, European Society of
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ACKNOWLEDGES
We thank the Syngenta Seeds for the economical support and the authorization to publish
these results.
FURTHER WORKS
Defects camera detection, camera size estimation evaluation.
Analysis of multispectral features from the other channels or combinations.
Fusion of acoustic and multispectral analysis and classification applied to the whole
storage period.
Application of this methodology to another products.
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SensorsInformation and Technology for Sustainable Fruit and Vegetable Production
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Évaluation de la capacité de stockage des melons de Piel de Sapo par la
fusion de sonde
Mots-clés : melons, réponse acoustique, multispectrale, fusion de sonde, fermeté, cavité
interne, maturité
Résumé
Plusieurs variétés de melon ont été évaluées selon leur capacité de stockage. Des
mesures destructives (le volume de la cavité interne, les solides solubles, la fermeté de
Magness-Taylor) et non destructives (la fermeté d'impact, la réponse acoustique, les
dispositifs multispectraux) ont été effectuées. La réponse acoustique montre une grande
variance sur une étendue de 78-225 hertz, diminuant quand le volume de la cavité et la
maturité augmente. Des images multispectrales étaient choisies dans la bande de
chlorophylle comme un complément approprié à la fréquence acoustique. La
classification non dirigée à la récolte avec un appareil photo multispectral est fortement
corrélée avec la fréquence acoustique et l'accélération d'impact. La fusion de la réponse
acoustique et de la classification multispectrale permet la différenciation des cavités
internes et la maturité.
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