Work place: KLE.Institute of Technology Hubli – 580 030, INDIA

E-mail: rajeshymath@gmail.com


Research Interests: Engineering, Computational Engineering


Rajesh Yakkundimath He has obtained Bachelor of Engineering in Instrumentation in 2004(VTU, Belgaum, INDIA) and M.Tech in Computer Science in 2007 (VTU, Belgaum, INDIA). He is working for his doctoral degree in Computer Science under VTU, Belgaum, INDIA. Since 2008 he is working as Assistant Professor in the department of Computer Science & Engineering, KLE.Institute of Technology, Hubli, INDIA.

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

By Jagadeesh D. Pujari Rajesh.Yakkundimath Abdulmunaf. Syedhusain. Byadgi

DOI: https://doi.org/10.5815/ijigsp.2014.01.04, Pub. Date: 8 Nov. 2013

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%

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