Fruit Disease Detection Using Convolution Neural Network Approach


  • Shivani Department of Computer Science, Regional Campus, Guru Nanak Dev University, Gurdaspur, India
  • Sharanjit Singh Department of Computer Science, Regional Campus, Guru Nanak Dev University, Gurdaspur, India



Universal filter, K mean clustering, MSE, CNN


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.


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How to Cite

Shivani, & Singh, S. (2018). Fruit Disease Detection Using Convolution Neural Network Approach. Asian Journal of Computer Science and Technology, 7(2), 62–65.