Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network

https://doi.org/10.22146/ijccs.16628

Kharis Syaban, Agus Harjoko,

(1) 
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta

Abstract


Compared with other methods of classifiers such as cellular and molecular biological methods, using the image of the leaves become the first choice in the classification of plants. The leaves can be characterized by shape, color, and texture; The leaves can have a color that varies depending on the season and geographical location. In addition, the same plant species also can have different leaf shapes. In this study, the morphological features of leaves used to identify varieties of pepper plants. The method used to perform feature extraction is a moment invariant and basic geometric features. For the process of recognition based on the features that have been extracted, used neural network methods with backpropagation learning algorithm. From the neural-network training, the best accuracy in classifying varieties of chili with minimum error 0.001 by providing learning rate 0.1, momentum of 0.7, and 15 neurons in the hidden layer foreach of various feature. To conduct cross-validation testing with k-fold tehcnique, obtained classification accuracy to be range of 80.75%±0.09% with k=4.


Keywords


leaf; chili; morphological; moment-invariant; backpropagation

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References

S. G. Wu, F. S. Bao, E. Y. Xu, Y.-X. Wang, Y. Chang, and Q. Xiang., 2007, A Leaf
Recognition Algorithm for Plant Classification Using Probabilistic Neural Network, Int.
Symp. Signal Process. Inf. Technol., pp. 1–6,.
[2] S. Mouine, I. Yahiaoui, and A. Verroust-blondet, 2013, A Shape-based Approach for
Leaf Classification using Multiscale Triangular Representation., ICMR,.
[3] T. Beghin, J. Cope, P. Remagnino, and S. Barman, 2010, Shape and texture based plant
leaf classification,.
[4] H. Syahputra and A. Harjoko, 2011, Klasifikasi Varietas Tanaman Kelengkeng
Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network dan
Probabilistic Neural Network,” IJCCS, vol. 55281, pp. 11–16.
[5] M. A. Agmalaro, A. Kustiyo, and A. R. Akbar, 2013, Identifikasi Tanaman Buah
Tropika Berdasarkan Tekstur Permukaan Daun Menggunakan Jaringan Syaraf Tiruan,”
Ilmu Komput. Agri-Informatika, vol. 2, pp. 73–82,.
[6] A. Kadir, L. E. Nugroho, A. Susanto, and P. I. Santosa, 2011, Leaf Classification Using
Shape, Color, and Texture Features,” Int. J. Comput. Trends Technol., pp. 225–230.
[7] J. Du, X. Wang, and G. Zhang, 2007, Leaf shape based plant species recognition, Appl.
Math. Comput., vol. 185, pp. 883–893,.
[8] A. Danti, M. Madgi, and B. S. Anami, 2012, Mean and Range Color Features Based
Identification of Common Indian Leafy Vegetables, vol. 5, no. 3, pp. 151–160.
[9] D. Putra, 2010, Pengolahan Citra Digital, I. Yogyakarta: ANDI,.
[10] Haykin, S., 2005. Neural Networks : A Comprehensive Foundation. Singapore: Pearson
Education.



DOI: https://doi.org/10.22146/ijccs.16628

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Indonesian Journal of Computing and Cybernetics Systems
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