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C=F>DAFig. 8. Transaxial (X–Y, top) and coronal (X–Z, bottom) slices of porosity
maps of (A) eggplant, (B) turnip, (C)apple and (D) pear, translated from thegrayscale CT images by means ofmoving average method with ROI
2.74 mm3. The porosity maps werepredicted by the single correlation
model for all products. Main parts ofthe products such as endocarp (EDC),epicarp (EPC), pedicel end (PE), groupof seeds (S), storage region (SR), coreregion (CR) vascular bundles, andcortex tissue (CT) are shown.

Nugraha: 2019 Non-destructive porosity mapping of fruit andvegetables
X-rayworks
CT.
Postharvest
Biologycortex zone
likely aﬀects andthe meanTechnology
porosity of turnip. The observed
signiﬁcantadvantage with using
respect to previous(Musseet al.,mean porosity was similar to that of another turnip cultivar
2010; Tanaka et al., 2018). On the other hand, determination of the ROI
150 (2019) 80–88(20.6 ± 0.3%) (Lech et al., 2018). The turnip porosity map may consize used in translating the stack of grayscale CT images into 3-D porosity maps by a moving average method was shown to be essential. For

tribute to a better understanding of characteristic of turnip micro-(ii)

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123Fig. 1 a Single frame color image of (i) sound and (ii) not-sound soybean seeds; b experimental set-up for visible imaging test (Stage-I) used inthis study: (i) flat-bed scanner; and (ii) sample holder

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Fig. 3 The different illustrations of the soybean seed: a regions in an X-ray image explaining the experimental quantization of the impact of theinternal cracks in soybean seed, b fluorescence image of soybean seed with the veins of the cotyledon and embryo visible (Kuensting et al. 2002)

Stage-I (visibleimaging)tests: classification
Mahajan = 2018 Machine vision
basedalternative
testing
approach
forpoorphysicalviability
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classification
technique andused in this research work is a
The seed samples
possessing
low-vigor exhibit
fieldthreelayered
feedforward
emergence (Behtarietal.
2014).
DemirandMavi(2008)
vigour testing of soybean seeds (Glycine max)Artificial. J Neural Network(ANN) and the system training of Stage-I was performed
reported that the low vigour condition of the seeds might
Sciby Technol
55:3949–3959
with 63 morphological, color and texture features that were
not necessarilyFood
be detected
standard germination
tests.
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