Asian Journal of Computer Science and Technology (AJCST)
Fruit Disease Detection Using Convolution Neural Network Approach
Author : Shivani and Sharanjit SinghVolume 7 No.2 July-September 2018 pp 62-65
Abstract
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.
Keywords
Universal filter, K mean clustering, MSE, CNN
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References
[1] Awate and D. Deshmankar, “Fruit Disease Detection using Color, Texture Analysis and ANN”, pp. 970–975, 2015.
[2] “Survey on Major Diseases of Vegetable and Fruit Crops Total Number of leaves / Fru it counted Sum of all disease ratings × 100
Total number of leaves / fru its × maximum rating value”, Vol. 35, No. September, pp. 423–429, 2010.
[3] J. P. Shah, “A Survey on Detection and Classification of Rice Plant Diseases”, 2016.
[4] M. Ilic, P. Spalevic, M. Veinovic, and A. A. M. Ennaas, “Data mining model for early fruit diseases detection”, pp. 910–913, 2015.
[5] M. Jhuria and A. Kum, “g for Smart Farming : Detection of Dise ase and Fruit Grading”, pp. 521–526, 2013.
[6] Engineering, A. Kumar, and G. S. Gill, “Automatic Fruit Grading and Classification System Using Computer Vision : A Review”, No. I, pp. 598–603, 2015.
[7] R. A. Sadek, “SVD Based Image Processing Applications : State of The Art, Contributions and Research Challenges”, Vol. 3, No. 7, pp. 26–34, 2012.
[8] Y. Zhang, Y. Liu, X. Li, and C. Zhang, “Salt and pepper noise removal in surveillance video based on low-rank matrix recovery,” Comput. Vis. Media, Vol. 1, No. 1, pp. 59–68, Aug. 2015.
[9] R. a.M, K. W.M, E. M. a, and W. Ahmed, “Jpeg Image Compression Using Discrete Cosine Transform – A Survey”, Int. J. Comput. Sci. Eng. Surv., Vol. 5, No. 2, pp. 39–47, 2014.
[10] Science and M. Studies, “Use of Digital Image Processing for Grain Counting”, pp. 6–9, 2015.
[11] M. Dewdney, “Fungal Diseases of Citrus Fruit and Foliage Foliar Fungal Diseases to be Covered”, IEEE 17th Int. Conf. Parallel Distrib. Syst., 2014.