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

The accuracy decreases when the number of varieties increases. We may resolve this
problem by increasing the feature vector‟s dimensions, i.e. to find more features, and use
better classification algorithm such as SVM [Wu et al., 2008]. The latter is a well known
machine learning method for classification. It is in fact the capacity of computer to learn from
examples. After having trained it by giving many leaf samples belonging to each grape
variety, the software can decide to which variety a new leaf belongs to.
Our prototype can only classify scanned images. In order to classify digital camera images,
we have to consider factors such as photography distance and focus. On the other hand, a
camera may take photos from different angles. This may allow us to distinguish the prostrate
hairs of a leaf from the erect hairs. However, this problem can be better resolved by a 3
dimensions camera or scanner. Based on the 3D image technique, we can more easily
calculate other leaf features such as the profile of leaf (OIV code 74 [OIV, 2009]). Light wave
lengths other than visible ones, such as infrared, microwave and terahertz [Lu, 2002; Xing
and Baerdemaeker, 2005] etc. can also be used to obtain digital leaf images. These images
should supply complementary features, useful for the variety identification. By combining
these mentioned techniques, we are expected to be able to calculate all the 45 codes selected
by OIV and hence to identify all grape varieties based on digital image processing by
computer.
This new identification method may not only simplify the identification procedure, but its
techniques can also improve the classical ampelographic identification method. The current
OIV Descriptor List [OIV, 2009] uses the code values in a qualitative way. For example, the
code 65 for the size of blade takes values 1, 3, 5, 7 and 9 which means respectively, very
small, small, medium, large and very large. Our method can calculate the size of blade in
inch 2 or cm 2 effectively and automatically. We may do this for all quantifiable codes of all
known varieties. With these quantitive values, we may give a value range for each current
qualitative value on one hand, and check if the code values for the 250 varieties in [OIV, 2000]
are coherent. These techniques can be used to detect new variety and guess the parent
varieties of a new hybrid variety. In fact, the feature vector of a new variety will not belong to
any known varieties, but a hybrid one should be close to its parents‟ ones. Computers
software can easily find out all the similar varieties and sort them according to the similitude.
IV. Conclusions
Based on the classical ampelographic grape identification method combined with machine
learning and pattern recognition techniques in computer science, we proposed a new cheap
and fast identification method via leaf image processing. We demonstrated its feasibility by
implementing a software prototype which could classify 354 leaf images belonging to 20
varieties with an accuracy of 87%.
We are continuing our research to increase both the number of grape varieties and the
accuracy by calculating more features from digital images and improving the classification
algorithms.