Plant Leaf Identification Based on Radial Basis Probabilistic Neural Network
DU JiXiang1,2,3, WANG ZengFu1
1.Department of Automation, University of Science and Technology of China, Hefei 2300262. Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 2300313. Department of Computer Science and Technology, Huaqiao University, Quanzhou 362021
Abstract In this paper, a plant species identification approach is proposed based on Gabor features and radial basis probabilistic neural network (RBPNN). The multiscale Gabor wavelet transform is applied to extract the texture features. The experimental results show that the RBPNN achieves high recognition rates and classification efficiency by using radial basis function neural network (RBFNN) for the plant species recognition and identification task.
[1] Ingrouille M J, Laird S M. A Quantitative Approach to Oak Variability in Some North London Woodlands. London Naturalist, 1986, 65: 3546 [2] Guyer D E, Miles G E, Schreiber M M, et al. Machine Vision and Image Processing for Plant Identification. Transactions of the ASAE, 1986, 29(6): 15001507 [3] Guyer D E, Miles G E, Gaultney L D, et al. Application of Machine Vision to Shape Analysis in Leaf and Plant Identification. Transactions of the ASAE, 1993, 36(1): 163171 [4] Timmermans A J M, Hulzebosch A A. Computer Vision System for Online Sorting of Pot Plants Using an Artificial Neural Network Classifier. Computers and Electronics in Agriculture, 1996, 15(1): 4155 [5] Rui Yong, She A C, Huang T S. Modified Fourier Descriptors for Shape Representation-A Practical Approach // Proc of the 1st International Workshop on Image Databases and Multimedia Search. Amsterdam, Netherlands, 1996: 2223 [6] Abbasi S, Mokhtarian F, Kittler J. Reliable Classification of Chrysanthemum Leaves through Curvature Scale Space // Proc of the 1st International Conference on ScaleSpace Theory in Computer Vision. Utrecht, Netherlands, 1997: 284295 [7] Mokhtarian F, Abbasi S. Matching Shapes with SelfIntersection: Application to Leaf Classification. IEEE Trans on Image Processing, 2004, 13(5): 653661 [8] Oide M, Ninomiya S. Discrimination of Soybean Leaflet Shape by Neural Networks with Image Input. Computers and Electronics in Agriculture, 2000, 29(1): 5972 [9] Fu Xing, Lu Hanqing. Preliminary Study on Automatical Plant Classification by Use of Computer. Chinese Journal of Ecology, 1994, 13(2): 6971 (in Chinese) (傅 星,卢汉清.应用计算机进行植物自动分类的初步研究.生态学杂志, 1994, 13(2): 6971) [10] Fu Hong, Chi Zheru, Chang Jie, et al. Extraction of Leaf Vein Features Based on Artificial Neural Network-Studies on the Living Plant Identification Ⅰ. Chinese Bulletin of Botany, 2004, 21(4): 429436 (in Chinese) (傅 弘,池哲儒,常 杰,等.基于人工神经网络的叶脉信息提取——植物活体机器识别研究Ⅰ.植物学通报, 2004, 21(4): 429436) [11] Otsu N. A Threshold Selection Method from GrayLevel Histograms. IEEE Trans on Systems, Man and Cybernetics, 1979, 9(1): 6266 [12] Ohlander R, Price K, Reddy D R. Picture Segmentation Using a Recursive Region Splitting Method. Computer Graphics and Image Processing, 1978, 8(3): 313333 [13] Chan F H Y, Lam F K, Zhu Hui. Adaptive Thresholding by Variational Method. IEEE Trans on Image Processing, 1998, 7(3): 468473 [14] Grigorescu S E, Petkov N, Kruizinga P. Comparison of Texture Features Based on Gabor Filters. IEEE Trans on Image Processing, 2002, 11(10): 11601167 [15] Huang Deshuang. Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence, 1999, 13(7): 10831101 [16] Zhao Wenbo, Yang Luyi, Wang Liming. The Hybrid Structure Optimization Algorithms of Radial Basis Probabilistic Neural Networks. Journal of System Simulation, 2004, 16(10): 21752180,2184 (in Chinese) (赵温波,杨鹭怡,王立明.径向基概率神经网络的混合结构优化算法.系统仿真学报, 2004, 16(10): 21752180,2184) [17] Huang Deshuang, Zhao Wenbo. Determining the Centers of Radial Basis Probabilistic Neural Networks by Recursive Orthogonal Least Square Algorithms. Applied Mathematics and Computation, 2005, 162(1): 461473 [18] Gonzalez R C, Wintz P. Digital Image Processing. 2nd Edition. Boston, USA: AddisonWesley, 1987
2008, Vol. 21 
