Identification of Houseplants Using Neuro-vision Based Multi-stage Classification System
الموضوعات : مجله گیاهان زینتیNarges Ghanei Ghoushkhaneh 1 , Abbas Rohani 2 , Mahmood Reza Golzarian 3 , Fatemeh Kazemi 4
1 - Department of Biosystems Engineering, Ferdowsi University of Mashhad, Iran
2 - Department of Biosystems Engineering, Ferdowsi University of Mashhad, Iran
3 - Department of Biosystems Engineering, Ferdowsi University of Mashhad, Iran
4 - Department of Horticultural Sciences and Landscape Engineering, Ferdowsi University of Mashhad, Iran
الکلمات المفتاحية: image processing, Artificial Neural Networks, Houseplants, identification, Multi-stage classification,
ملخص المقالة :
In this paper, we present a machine vision system that was developed on the basis of neural networks to identify twelve houseplants. Image processing system was used to extract 41 features of color, texture and shape from the images taken from front and back of the leaves. The features were fed into the neural network system as the recognition criteria and inputs. Multilayer perceptron (MLP) neural network with Declining Learning-Rate Factor algorithm (BDLRF) training algorithm was used as a classifier. Classification was done in three stages based on eligibility and strength of characteristics in identifying the plants. Eligibility criteria were assessed at each stage using plants class resolution power. In this classification method, each step requires a small number of attributes and for this reason its speed and accuracy can be very high. The results showed that the accuracy of classification of plants in three steps reaches 100%. Also, the optimal features for classification included three inputting steps of morphological features, HSI color features extracted from back of the leaves, and HSI texture features of the back of the leaves.
Ahmed, F., Al-Mamun, H. A., Hossain Bari, A. S. M., Emam Hossain, and Kwan, P. 2012. Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection, 40: 98-104.
Chaudhary, P., Chaudhari, A. K., Cheeran, A. N. and Godara, S. 2012. Color transform based approach for disease spot detection on plant leaf. International Journal of Computer Science and Telecommunications, 3: 65-70.
Golzarian, M., Ghanei, N., Sadeghi, F. and Kazemi, F. 2014. A qualitative and quantitative approach to assessing the performance of contrast enhancing colour indices used in automatic computer vision plant identification system. The 8th National Conference in Biosystems Engineering, Mashhad, 29-31 January 2014, Iran (In Persian).
Gonzalez, R. C., Woods, R. E. and Eddins, S. L. 2009. Digital image processing using MATLAB. 2nd ed. Upper Saddle River, NJ, US: Pearson Prentice Hall.
Guyer, D., Miles, G., Schreiber, M., Mitchell, O. and Vanderbilt, V. 1986. Machine vision and image processing for plant identification. Transactions of the ASAE, 29 (6):1500-1507.
Jafari, A., Mohtasebi, S., Eghbali Jahromi, H. and Omid, M. 2004. Color feature extraction by means of discriminant analysis for weed segmentation. ASAE/CSAE Annual International Meeting. Fairmont Chateau Laurier, The Westin, August 1-4. Government Centre Ottawa, Ontario,Canada.
Li, Y., Chi, Z. and Feng, D. D. 2006. Leaf vein extraction using independent component analysis. IEEE International Conference on Systems, Man, and Cybernetics. October 8-11. Taipei, Taiwan.
Meyer, G. E. and Neto, J. C. 2008. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 63: 282-293.
Mahmoudi, M., Khazayi, J. and Vahdati, K. 2008. Identification of wulnut genotypes using image processing and neural network techniques. The World Conference on Agricultural Information and IT, IAALD AFITA WCCA 2008, at Tokyo, Japan. 2 pages.
Neto, J. C., Meyer, G. E., Jones, D. D. and Samal, A. K. 2005. Plant species identification using elliptic fourier leaf shape analysis. Computers and Electronics in Agriculture, 50: 121-134.
Pramanik, S., Bandyopadhyay, S. K., Bhattacharyya, D. and Kim, T. 2010. Identification of plant using leaf image analysis. Signal Processing and Multimedia, Communications in Computer and Information Science, 123: 291-303.
Rohani, A., Abbaspour-Fard, M. H. and Shamsolla, A. 2011. Prediction of tractor repair and maintenance costs using artificial neural network. Expert Systems with Applications 38, 8999-9007.
Sanei Shariat Panahi, M. and Fayaz, M. 1985. Growing, maintenance and proliferation of houseplants. Sepehr Publication, Tehran.
Vakil-Baghmisheh, M.T. and Pavešic, N. 2001. Back-propagation with declining learning rate. Proceeding of the 10th Electrotechnical and Computer Science Conference, Portoroz, Slovenia, Vol. B, pp. 297–300.
Wang, X. F., Huang, D. S., Du, J., Xu, H. and Heutte, L. 2008. Classification of plant leaf images with complicated background. Applied Mathematics and Computation, 205: 916-926.
White, S., Fein, S. and Kopylec, J. 2006. Virtual vouchers: prototyping a mobile augmented reality user interface for botanical species identification. IEEE Symposium on 3D User Interfaces. March 25-26. Alexandria, Virginia, USA.
Wu, S. G., Bao, F. S., Xu, E. Y., Wang, Y., Chang, Y. and Xiang, Q. 2007. A leaf recognition algorithm for plant classification using probabilistic neural network. IEEE International Symposiumon Signal Processing and Information Technology. December 15-18. Giza. Egypt.
Zheng, X. and Wang, X. 2010. Leaf vein extraction using a combined operation of mathematical morphology. 2nd International Conference on Information Engineering and Computer Science (ICIECS).
