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

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Jagadeesh D. Pujari 1,* Rajesh.Yakkundimath 2 Abdulmunaf. Syedhusain. Byadgi 3

1. S.D.M.College of Engineering & Technology Dharwar – 580 008, INDIA

2. KLE.Institute of Technology Hubli – 580 030, INDIA

3. University of Agricultural Sciences, Dharwar – 580005, INDIA

* Corresponding author.


Received: 2 Aug. 2013 / Revised: 2 Sep. 2013 / Accepted: 27 Sep. 2013 / Published: 8 Nov. 2013

Index Terms

Fungal disease, Discrete wavelet transform, Principal component analysis, Mahalanobis distance, Probabilistic neural network


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%

Cite This Paper

Jagadeesh D. Pujari, Rajesh.Yakkundimath, Abdulmunaf. Syedhusain. Byadgi,"Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops", IJIGSP, vol.6, no.1, pp.24-31, 2014. DOI: 10.5815/ijigsp.2014.01.04


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