Identification of Grape Varieties via Digital Leaf Image ... - Oiv2010.ge

I. Introduction
There are over 10,000 grape varieties throughout the world. About 3000 of them are widely
cultivated in production and many are wine varieties [Zhai, 2001]. Grape variety
identification is of great significance for resource statistics, new specie detection and
protection of genetic resources. OIV‟s Strategic Framework includes the task for recognising
new viticultural varieties [OIV, 2005].
The classical identification method is based on ampelography [Galet,1990; Tassie and
Blieschke, 2008]. Some new methods have been also developed recently, using different
approaches such as DNA molecular genetic marker [Bower et al., 1993; Zhang et al., 1996;
Testier et al., 1999], pollen morphology [Wang and Li, 2000], anthocyanin analysis
[Wendelin and Barna, 1994], etc. All these methods need expert intervention and are hence
quite expensive. Some of them need special devices and take a long time. Today, computer
technologies have a wide range of applications in many fields including grape production.
There are many successful examples where the computer has been used for image processing
[Li et al., 2007; Barbu, 2009] and identification of plant species [Ye et al., 2004] based on
pattern recognition. We look for a new method for identifying grape varieties combining the
computer techniques and the classical ampelography. Based on the processing of digital grape
leaf image, this new method would be rapid, efficient and nearly automatic with little or even
no human intervention. Our research objective is to develop a software product, available on
web, which will be able to tell a browser the variety of the grape leaf image that s/he uploads.
The ampelographic identification of grape varieties is based on the observation of features
on some organs of a grape, such as flower, berry, shoot and leaf. OIV has produced 2 editions
[OIV, 1983; OIV, 2009] of the document “OIV descriptor list for grape varieties and Vitis
species” which defines as a standard the ampelographic characteristics for the identification of
Vitis varieties and species. Using the 128 characteristics selected by [OIV, 1983] where each
characteristics is signed a code and may take values from 1 to 9 for all grapes, [OIV, 2000]
describes 250 wine grape varieties of its member states, by assigning a values to descriptor
codes for each variety . For a given grape sample, if each its code has the same value as the
variety V of the 250 in [OIV, 2000], this grape‟s variety is classified as V. All ampelographic
experts agreed that the features of mature leaf are the most determinate for the varieties
identification. For the 128 codes, 35 of them are for leaf and 29 for mature leaf. [OIV, 2009]
adds another 18 codes from 601 to 618 on mature leaf. On the “Primary descriptor priority list”
of 14 codes, there are 9 on leaf.
In our new approach based, the main idea is to let computer calculate all the code values
instead of measuring them by a human being. Then the computer can compare these values
against the known ones as in [OIV, 2000] to find the right variety. However, on one hand, it‟s
not easy to calculate some code values and on the other hand, it is not necessary to know all
these values for the identification purpose. Furthermore, some features not selected by [OIV,
2009] may also contribute to distinguish or identify varieties, for example, Hu's moment
invariants for an image [Hu, 1962].