Feature Selection Techniques for the Classification of Leaf Diseases in Turmeric

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-43 Number-3
Year of Publication : 2017
Authors : Pream Sudha V
  10.14445/22312803/IJCTT-V43P121

MLA

Pream Sudha V  "Feature Selection Techniques for the Classification of Leaf Diseases in Turmeric". International Journal of Computer Trends and Technology (IJCTT) V43(3):138-142, January 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Crop maintenance is one of the crucial factors that determine the quantity and quality of the agricultural products. Protecting crops from plant diseases is an important aspect that increases the profit of the farmer. This study aims at developing a computational model that will facilitate crop production by accurately identifying diseases that affect productivity of turmeric plants. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot, and leaf blotch. This system uses technologies such as feature selection and machine learning techniques for the identification and classification of diseases in turmeric leaf. Principal component analysis, Information gain and Relief-f attribute evaluator methods were investigated in combination with machine learning algorithms like Support Vector Machine, Decision Tree and Naïve Bayes. The performance of the models were evaluated using 10 fold cross validation and the results were reported. Comparatively, the model using SVM applied to features selected using Information gain performed well with an accuracy of 93.75.

References
[1] P. Revathi, M. Hemalatha, Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method, International Journal of Scientific Engineering and Technology, Volume No.3, Issue No.1 , ISSN : 2277-1581.
[2] Yan Cheng Zhang, Han Ping Mao, Bo Hu, Ming Xili,“Features selection of Cotton disease leaves image based on fuzzy featureselection techniques”, IEEE Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2-4.2007.
[3] Bernardes.A.A, J.G.Rogeri, N.Marranghello, A. S. Pereira, A.F. Araujo and João Manuel R. S. Tavares. “Identification of Foliar Diseases in Cotton Crop”. SP, Brazil.
[4] Mr. Hrishikesh P. Kanjalkar, Prof. S.S.Lokhande, “Feature Extraction of Leaf Diseases”, International Journal of Advanced Research in Computer Engineering & Technology, Volume 3, Issue 1, January 2014.
[5] P.Revathi, M.Hemalatha, “Identification of Cotton Diseases Based on Cross Information Gain Deep Forward Neural Network Classifier with PSO Feature Selection”, International Journal of Engineering and Technology, Vol 5 No 6 Dec 2013-Jan 2014, ISSN : 0975-4024.
[6] Xinhong Zhang , Fan Zhang, “Images Features Extraction of Tobacco Leaves”, CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02, p 773-776.
[7] Gulhane.V. A & A. A. Gurjar, “Detection of Diseases on Cotton Leaves and Its Possible Diagnosis” (IJIP), 5 (5): 591-598. 2011.
[8] Ajay A. Gurjar and Viraj A. Gulhane, “Disease Detection On Cotton Leaves by Eigenfeature Regularization and Extraction Technique”. IJECSCSE.1 (1): 1-4.2012.
[9] Mrunalini R. Badnakhe and Prashant R. Deshmukh..”Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering”.2(3): 449-452. 2012.
[10] Arivazhagan.S , Newlin Shebiah.R, Ananthi.S. Vishnu Varthini.S, 2013, "Detection of unhealthy regions of plant leaves and classification of plant leaf diseases using texture features”, Agric Eng Int: CIGR Journal, 15 (1):211-217.
[11] F.J. Ferri, P. Pudil, M. Hatef, and J. Kittler, 1994. “Comparative study of techniques for largescale feature selection,” in: E. S. Gelsema and L. S.Kanal, eds., Pattern Recognition in Practice IV, Multiple Paradigms,Comparative Studies and Hybrid System (Elsevier, Amsterdam, ) : 403–413.
[12] Meunkaewjinda. A, P.Kumsawat, K.Attakitmongcol and A.Sirikaew.”Grape leaf disease Detection n from color imaginary using Hybrid intelligent system”, Proceedings of ECTI-CON. 2008.

Keywords
Leaf disease, Turmeric, Machine Learning, Feature Selection.