.: Click here to download :.
Plants exist everywhere we live, as well as places without us.
Many of them carry significant information for the development of human society.
The urgent situation is that many plants are at the risk of extinction. So it is very necessary
to set up a database for plant protection. We believe that the first step is to teach a computer
how to classify plants. Compared with other methods, such as cell and molecule biology methods,
classification based on leaf image is the first choice for leaf plant classification. Sampling leaves
and photoing them are low-cost and convenient. One can easily transfer the leaf image to a computer
and a computer can extract features automatically in image processing techniques. Some systems employ
descriptions used by botanists. But it is not easy to extract and transfer those features to a
computer automatically.
We have developed an efficient algorithm for leaf classification that combines high-order statistics of
image features together with shape information and neural network as nonlinear classifier. The code has
been tested with FLAVIA database achieving an excellent recognition rate of 92.09% (32 classes,
40 training images and the remaining images used for testing for each class, hence there are 1280
training images and 627 test images in total randomly selected and no overlap exists between the
training and test images).
FLAVIA source code and dataset are available at this URL http://flavia.sourceforge.net.
Our approach outperforms this algorithm and moreover it does not require any human interfered part.
In FLAVIA algorithm in fact you need to mark the two terminals of the main vein of the leaf via mouse click.
The distance between the two terminals is defined as the physiological length.
Stephen Gang Wu, Forrest Sheng Bao, Eric You Xu, Yu-Xuan Wang, Yi-Fan Chang and Chiao-Liang Shiang,
A Leaf Recognition Algorithm for Plant classification Using Probabilistic Neural Network, IEEE 7th
International Symposium on Signal Processing and Information Technology, Dec. 2007, Cairo, Egypt.
Index Terms: Matlab, source, code, neural network, feature extraction, leaf recognition, plant classification.
 |
Figure 1. Leaf |
||||||||||||||
|
A simple and effective source code for Leaf Recognition System. |
|||||||||||||||
|
|
Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Signal Processing Toolbox and Matlab Neural
Network Toolbox are required.
|
||||||||||||||
|
Release |
Date |
Major features |
|||||||||||||
|
1.0 |
2009.04.09 |
|
|||||||||||||
We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
|||||||||||||||
Leaf Recognition System. Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 1300 EUROS (less
than 1820 U.S. Dollars). |
|||||||||||||||
Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of Leaf Recognition System. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
The authors have no relationship or partnership
with The Mathworks. All the code provided is written in Matlab
language (M-files and/or M-functions), with no dll or other
protected parts of code (P-files or executables). The code was
developed with Matlab 2006a. Matlab Image Processing Toolbox, Matlab Signal Processing
Toolbox and Matlab Neural Network Toolbox are required.
The code provided has to be considered "as is" and it is without any kind of warranty. The
authors deny any kind of warranty concerning the code as well
as any kind of responsibility for problems and damages which may
be caused by the use of the code itself including all parts of
the source code.
