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

Abstract

This paper describes automatic detection and classification of v isual 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 algorith ms are used to preprocess, segment, extract and reduce features fro m fungal affected parts of a crop. The feature extract ion is done with discrete wavelet transform (DWT) and features are further reduced by using Principal co mponent 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%.