Open AccessFruit Disease Detection Using Convolution Neural Network Approach
Author(s) -
. Shivani,
Sharanjit Singh
Publication year - 2018
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2018.7.2.1871
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , cluster analysis , noise (video) , filter (signal processing) , feature extraction , convolutional neural network , feature (linguistics) , convolution (computer science) , median filter , margin (machine learning) , artificial neural network , gaussian filter , gaussian blur , image (mathematics) , image processing , computer vision , machine learning , image restoration , linguistics , philosophy
Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.