Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops

摘要

This paper describes automatic detection and classification of visual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorithms are used to preprocess, segment, extract and reduce features from fungal affected parts of a crop. The feature extraction is done with discrete wavelet transform (DWT) and features are further reduced by using Principal component analysis (PCA). Reduced features are then used as inputs to classifiers and tests are performed to classify image samples. We have used statistical based Mahalanobis distance and Probabilistic neural network (PNN) classifiers. The average classification accuracies using Mahalanobis distance classifier are 83.17% and using PNN classifier are 86.48%. Reference [1]J.D. Pujari, Rajesh.Yakkundimath, A.S.Byadgi (2013), “Grading and Classification of anthracnose fungal disease in fruits”, International Journal of Advanced Science and Technology, Vol.52. [2]Arman Arefi and Asad Modarres Motlagh (2013) , “Development of an expert system based on wavelet transform and artificial neural networks for the ripe tomato harvesting robot”, AJCS 7(5):699-705, ISSN:1835-2707. [3]Heena Patel and Saurabh Dave (2012), “An application of Radon and Wavelet Transforms for Image Feature Extraction”, International Journal of Electronics and Communication Engineering, Volume 1,Issue 2, Pages 1-8. [4]Namita Aggarwal, R. K. Agrawal (2012), “First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images”, Journal of Signal and Information Processing, 2012, 3, 146-153 doi:10.4236/jsip.2012.32019 Published Online May 2012 (http://www.SciRP.org/journal/jsip). [5]A. Phinyomark, A. Nuidod, P. Phukpattaranont and C. Limsakul (2012), “Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification”, Electronics and Electrical Engineering. – Kaunas: Technological, No. 6(122). – P. 27–32. [6]Y. Zhang* and L. Wu (2012), “An MR Brain images classifier via principal component analysis and kernel support vector machine”, Progress In Electromagnetics Research, Vol. 130, 369-388. [7]Jayamala K. Patil, Raj Kumar (2011), “Advances in image processing in image processing for detection of plant diseases”, Journal of Advanced Bioinformatics Applications and Research Volume 2, Issue 2, Pages 135-141. [8]Lili N.A, F. Khalid, N.M. Borhan (2011), “Classification of Herbs Plant Diseases via Hierarchical Dynamic Artificial Neural Network after Image Removal using Kernel Regression Framework”, International Journal on Computer Science and Engineering Vol. 3 No.1. [9]D. Moshou, C. Bravo , R. Oberti , J.S. West , H. Ramon , S. Vougioukas , D. Bochtis (2011), “Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops”, Biosystems Engineering (1 0 8) Pages 3 1 1 -3 2 1. [10]D S Guru, P B Mallikarjuna, S Manjunath (2011), “Segmentation and Classification of Tobacco Seedling Diseases”, COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference. [11]Basvaraj .S. Anami, J.D.Pujari and Rajesh.Yakkundimath (2011), “Identification and Classification of Normal and Affected Agriculture/horticulture Produce Based on Combined Color and Texture Feature Extraction”, International Journal of Computer Applications in Engineering Sciences, Vol 1, Issue 3. [12]H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and ALRahamneh (2011), “Fast and Accurate Detection and Classification of Plant Diseases”, International Journal of Computer Applications, Volume 17– No.1. [13]Miroslaw Miciak (2010), “Radon Transformation and Principal Component Analysis Method Applied in Postal address recognition task”, International Journal of Computer Science and Applications, Vol. 7 No. 3, pp. 33 – 44. [14]E.-S. A. Dahshan, T. Hosny and A.-B. M. Salem (2010), “A Hy- brid Technique for Automatic MRI Brain Images Classification,” Digital Signal Processing, Vol. 20, No. 2, pp. 433-441. [15]A. Camargo, J.S. Smith (2009), “Image pattern classification for the identification of disease causing agents in plants”, Computers and Electronics in Agriculture (66) Pages 121–125. [16]Qing Yao, Zexin Guan, Yingfeng Zhou, Jian Tang, Yang Hu and Baojun Yang (2009), “Application of support vector machine for detecting rice diseases using shape and color texture features”, International Conference on Engineering Computation. [17]Mehdi Lotfi, Ali Solimani, Aras Dargazany, Hooman Afzal, Mojtaba Bandarabadi (2009), “Combining Wavelet Transforms and Neural Networks for Image Classification”, 41st Southeastern Symposium on System Theory University of Tennessee Space Institute Tullahoma, TN, USA, March 15-17. [18]Geng Ying, Li Miao, Yuan Yuan and Hu Zelin (2008), “A Study on the Method of Image Pre-Processing for Recognition of Crop Diseases”, International Conference on Advanced Computer Control. [19]Kuo-Yi Huang (2007