TABLE I
DETAILS ABOUT THE LEAF NUMBERS OF DIFFERENT TYPES OF PLANTS

Scientific Name(in Latin) Common Name training samples number of incorrect recognition Phyllostachys edulis (Carr.) Houz. pubescent bamboo 58 0 Aesculus chinensis Chinese horse chestnut 63 0

![0_image_0.png](0_image_0.png)

Populus ×*canadensis* Moench Carolina poplar 58 3 Liriodendron chinense (Hemsl.) Sarg. Chinese tulip tree 50 0 Citrus reticulata Blanco tangerine 51 0
sample in training set is the j-th kind of plant, then we have one 1 on the j-th row of i-th column of M.

## V. Experimental Result

To each kind of plant, 10 pieces of leaves from testing sets are used to test the accuracy of our algorithm. Numbers incorrect recognition are listed in the last column of Table I. The average accuracy is 90.312%.

Some species get a low accuracy in Table I. Due to the simplicity of our algorithm framework, we can add more features to boost the accuracy.

We compared the accuracy of our algorithm with other general purpose (not only applicable to certain species) classification algorithms that only use leaf-shape information.

According to Table II, the accuracy of our algorithm is very similar to other schemes. Considering our advantage respect to other automated/semi-automated general purpose schemes, easy-to-implement framework and fast speed of PNN, the performance is very good.

The source code in MATLAB can be downloaded now from http://flavia.sf.net.

## Vi. Future Work

Since the essential of the competitive function is to output the index of the maximum value in an array, we plan to let our algorithm output not only the index of maximum value, but also the indices of the second greatest value and the third greatest value. It is based on this consideration that the index

![0_image_1.png](0_image_1.png)

![0_image_2.png](0_image_2.png)

of the second greatest value corresponds to the second top matched plant. So does the index of the third greatest value. Sometimes, maybe the correct plant is in the second or the third most possible plant. We are going to provide all these three possible answers to users. Further more, users can choose the correct one they think so that our algorithm can learn from it to improve its accuracy.

Other features are also under consideration. Daniel Drucker from Department of Psychology, University of Pennsylvania, suggested us to use Fourier Descriptors so that we can do some mathematical manipulations later. We are also trying to use other features having psychology proof that is useful for human to recognize things like the leaf, such as the surface qualities [30].

Our plant database is under construction. The number of

| ACCURACY COMPARISON Scheme Accuracy proposed in [2] 71% 1-NN in [17] 93%   |      |
|----------------------------------------------------------------------------|------|
| k-NN (k = 5) in [17]                                                       | 86%  |
| RBPNN in [17]                                                              | 91%  |
| MMC in [1]                                                                 | 91%  |
| k-NN (k = 4) in [1]                                                        | 92%  |
| MMC in [18]                                                                | 92%  |
| BPNN in [18]                                                               | 92%  |
| RBFNN in [22]                                                              | 94%  |
| MLNN in [22]                                                               | 94 % |
| Our algorithm                                                              | 90%  |
