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Home»Articles»Fruit Disease Detection Using Convolution Neural Network Approach

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Fruit Disease Detection Using Convolution Neural Network Approach

Author : Shivani and Sharanjit Singh
Volume 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

Full Text:

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.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

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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.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
hc[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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