Additional info 1:
Multi-perspective imaging angle, distance and number
With the change of the intersection angle between two mirrors, the camera can get three or
five objects as follows shown. The captured image contained one more objects and represented
different sides information of object. The angles between those projective planes of object were
changed with the change of angle β, as well as the imaging distance. In order to get a full surface
of the object, a proper  or β was required to project the object‘s side-information.
If to get three objects in one image, the angle of those projective planes is 120°. If to get five
objects in one image, the angle θ of those projective planes is 72°.
Figure 1 Examples of multi--perspective imaging method
Figure 2 Imaging distance of actual and virtual objects
Additional Info 2
The angle between the images
Why is the angle between three image set to 120° ?
When one image of three bunches was captured, the structure of grape cluster could be
consisted by their contours as fig.3a shown. One of the transaction in the cluster could be
presented as fig.3b shown, and was expressed by the three projective contours as fig.3c shown.
This was on the basis of the assumption that the area of fig.3b was equal to the area of hexagon in
fig.3c. Thus the area S can be inferred as follows:
s
1
 (l1  l2  sin   l2  l3  sin   l3  l4  sin(     )  ...)
2
（EQ 1）
Where 、β、--β are the intersection angle between the contours F1, F2, F3. li is the radius
of transaction. EQ1 was trimmed to EQ2.
2s  (l1  l2  l4  l5 )sin   (l2  l3  l5  l6 )sin   (l3  l4  l6  l1 )sin(   )
2s  1 sin    2 sin    3 sin(   )
(EQ2)
Due to the random placement of the grape cluster, the probability of each contour’s radius has
the trend of equality, and it can be expressed as p(1 )  p( 2 )  p( 3 ) . Thus, here  was required
to be maximized,
  sin   sin   sin(   )
Obviously when
  60,   60 ,  was the max. That is namely when one image of
three bunches was captured in every 120°, the contained surface information was the maximum.
Figure 3 Projective plane of grape cluster
Additional Info 3
The estimation of grape compactness was lack in China
In Chinese National Standard about the table grape, there are no detail descriptions on the
compactness of grape cluster. The compactness was taken as an index to classify the disqualified
cluster. Only the compactness of grape cluster that was overloose or overtight, was classified to
the disqualified category. This manual judgment was undertaken by our technician. Further work
will focus on the compactness of grape cluster referring to the OVI (2007).
Reference
NY/T 470-2001, table grape [S].
GH/T 1022-2000, table grape [S]
OVI (2007). World Vitivinicultural Statistics 2007 — Structure of the World Vitivinicultural Industry 2007
[Online
International
Organization
of
Vine
and
Wine].
Available
at
http://news.reseauconcept.net/images/oiv_uk/Client/Statistiques_commentaires_annexes_2007_EN.pdf
Cubero S, Diago MP, Blasco J, Tardáguila J, Millán B, Aleixos N (2014) A new method for pedicel/peduncle
detection and size assessment of grapevine berries and other fruits by image analysis Biosyst Eng
117:62-72 doi:http://dx.doi.org/10.1016/j.biosystemseng.2013.06.007
Cubero S et al. (2015) A new method for assessment of bunch compactness using automated image analysis
Australian Journal of Grape and Wine Research 21:101-109 doi:10.1111/ajgw.12118
Additional info 4:
Pixel-size conversion
Reference measurement was conducted before imaging grapes. Four iron balls (standard
component) were attracted by a magnet, which was hung on the spring clip, and the diameters of iron
balls from top to bottom were: 40mm, 30mm, 25mm and 20mm respectively. With image processes
approach, the fitting circles were drawn on Fig.5a, and the conversion pixel to mm was regressed by a
linear model with a robust correlation of 0.9999 (Fig.4b). This linear conversion can be used to
measure the bunch or berry size later. After consideration, this fig.4 was not put in the manuscript, and
here taken as a supplementary instruction.
Actual size (mm)
45
y = 0.3648x
R2 = 0.9997
35
25
15
50
(a) the fitting circles
70
90
110
Image size (pixel)
(b) linear regression
Fig.4 Pixel-mm conversion