LEAF DISEASE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK
DOI:
https://doi.org/10.11113/jt.v77.6463Keywords:
Leaf disease, phyllanthus elegans wall, image prosessingAbstract
Nowadays, herb plants are importance to medical field and can give benefit to human. In this research, Phyllanthus Elegans Wall (Asin-Asin Gajah) is used to analyse and to classify whether it is healthy or unhealthy leaf. This plant was chosen because its function can cure breast cancer. Therefore, there is a need for alternative cure for patient of breast cancer rather than use the technology such as Chemotherapy, surgery or use of medicine from hospital. The purpose of this research to identify the quality of leaf and using technology in agriculture field. The process to analysis the leaf quality start from image acquisition, image processing, and classification. For image processing method, the most important for this part is the segmentation using HSV to input RGB image for the color transformation structure. The analysis of leaf disease image is applied based on colour and shape. Finally, the classification method use feed-forward Neural Network, which uses Back-propagation algorithm. The result shows comparison between Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) and comparison between MLP and RBF shown in percentage of accuracy. MLP and RBF is algorithm for Neural Network. Conclusively, classifier of Neural Network shows better performance and more accuracy.
References
K. Patil. Jayamala, and Raj Kumar. 2011. Color Feature Extraction of Tomato Leaf Diseases. International Journal of Engineering Trends and Technology. 21(2): 72-74.
Arivazhagan, S. et al. 2013. Detection Of Unhealthy Region Of Plant Leaves And Classification Of Plant Leaf Diseases Using Texture Features. CIGR Journal. 15(1): 211-217.
2013. Automation Detection And Classification Of Plant Diseases Through Image Processing. International Journal of Advance Research in Computer Science and Software Engineering. 3(7): 798-874.
N. Ghaiwat, Savita, and Parul Arora. 2014. Detection and Classification of Plant Leaf Diseases Using Image Processing Techniques: A Review. Department of Electronics and Telecommunication Engineering, Wagholi, Pune. 2(3): 1-7.
Pranom Chantaranothai. 2005. Taxonomic Notes on the genus Phyllanthus L. (Euphorbiaceae) in Thailand. Thai Forest Bulletin (Botany). 33: 16-20.
Saad, Hashim. 2015. Detail Of Botanical Garden Perlis And About Asin-Asin Plant. In person.
Zhi-Chun Huang, Patrick P. K. Chan, Wing. W. Y. Ng, Daniel S. Yeung. 2010. Content Based Image Retrieval Using COLOR Moment And Gabor Texture Feature. Proceedings of 9th International Conference On Machine Learning And Cybernetics, Qingdao, 11-14 July. 719-724.
R. Brunelli and O. Mich. 2001. Histograms Analysis for Image Retrieval. Pattern Recognition. 34(8): 1625-1637.
J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih. 1997. Image Indexing Using Color Correlograms. IEEE Int. Conf. Computer Vision and Pattern Recognition, San Juan, Puerto Rico. 762-768.
Jau-Ling Shih & Ling-Hwei Chen. Color Image Retrieval Based On Primitives Of Color Moments.
2001. ISO/1EC JTC 1/SC29/WG 11/N4062: MPEG-7 requirements document.
Tajane, Vinita, and N. J. Janwe. 2014. Medicinal Plants Disease Identification Using Canny Edge Detection Algorithm, Analysis and CBIR. International Journal of Advance Research in Computer Science and Software Engineering. 4(6): 530-536.
2014. Detection And Classification Of Plant Diseases. International Journal of Advance Research in Computer Science and Software Engineering. 2(6): 868-874.
M. S. Prasad Babu and B. Srinivasa Rao. 2007. Leaves Recognition Using Back Propagation Neural Network-Advice For Pest and Disease Control On Crops. IndiaKisan.Net: Expert Advissory System.
Paulraj. Pandiyan. 2014. Artificial Neural Network. School of Mechatronic Engineering, Unimap.
Raad, Ali, Ali Kalakech, and Mohammad Ayache. 2015. Breast Cancer Classification Using Neural Network Approach: Mlp And Rbf. International Arab Conference on Information Technology. 10(13): 15-19.
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