Texture Analysis of Fruits for Its Deteriorated Classification

  • Deepanshi Singla
  • Abhilasha Singh
  • Ritu Gupta
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 18)


Due to growing requirement in agriculture industry, the need to effectively grow a plant and increase in yield is very important. In order to attain more value added goods, a quality control is essentially required. Assessment as well as segregation of fruits is generally based on manual observations. This process can be automated using image processing techniques. The ability to identify the quality of fruits is the most significant trait while designing an automatic fruit categorization machine in order to save considerable human effort. This paper proposes a technique which will diagnose whether the fruit is fresh or rotten and classify the decayed fruit on the basis of pre-decided grading criterion. In proposed work, images are classified on the basis of colour, texture and morphology. Proposed framework is modelled into three parts of image processing which includes texture and feature extraction using morphology, image segmentation using threshold and fruit grading. This software can be a great help for fruit business industry as it will automate the fresh fruit selection process and hence increase the speed of selecting quality product.


Image processing Morphology Fruit disease detection Fruit grading 


  1. 1.
    M. Jhuria, A. Kumar, R. Borse, Image processing for smart farming: detection of disease and fruit grading, in IEEE Second International Conference on Image Information Processing (ICIIP), pp. 521–526, 2014Google Scholar
  2. 2.
    A. Awate, D. Deshmankar, G. Amrutkar, U. Bagul, S. Sonavane, Fruit disease detection using color, texture analysis and ANN, in International Conference on Green Computing and Internet of Things, pp. 970–975, 2015Google Scholar
  3. 3.
    S.R. Dubey, A.S. Jalal, Detection and classification of apple fruit diseases using complete local binary patterns, in Third International Conference on Computer and Communication Technology (ICCCT), pp. 346–351, 2012Google Scholar
  4. 4.
    M. Dhakate, A.B. Ingole., Diagnosis of pomegranate plant diseases using neural network, in Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4, 2015Google Scholar
  5. 5.
    K.R. Gavhale, U. Gawande, K.O. Hajari, Unhealthy region of citrus leaf detection using image processing techniques, in International Conference for Convergence of Technology (I2CT), pp. 1–6, 2014Google Scholar
  6. 6.
    S.M. Iqbal, A. Gopal, A.B. Nair, P.E. Sankaranarayanan, Estimation of size and shape of citrus fruits using image processing for automatic grading, in Third International Conference on Signal Processing ,Communication and Networking (ICSCN), pp. 1–8, 2015Google Scholar
  7. 7.
    J.P. Shah, H.B. Prajapati, V.K. Dabhi, A survey on detection and classification of rice plant diseases, in IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp. 1–8, 2016Google Scholar
  8. 8.
    M.R. Satpute, S.M. Jagdale, Automatic fruit quality inspection system, in International Conference on Inventive Computation Technologies (ICICT), pp. 1–4, 2016Google Scholar
  9. 9.
    H. Afrisal, M. Faris, G.P. Utomo, L. Grezelda, I. Soesnti, M.F. Andri, Portable smart sorting and grading machine for fruits using computer vision, in International Conference on Computer, Control, informatics and Its Applications (IC3INA), pp. 71–75, 2013Google Scholar
  10. 10.
    H. Dang, J. Song, Q. Guo, A fruit size detecting and grading system based on image processing, in Second International Conference on Intelligent Human-machine Systems and Cybernetics (IHMSC), pp. 83–86, 2010Google Scholar
  11. 11.
    V.H. Pham, B.R. Lee, An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm. Vietnam J. Comput. Sci. 2(1), 25–33 (2015)CrossRefGoogle Scholar
  12. 12.
    J.G.A. Barbedo, Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2, 660 (2013.) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Deepanshi Singla
    • 1
  • Abhilasha Singh
    • 1
  • Ritu Gupta
    • 1
  1. 1.Amity School of Engineering and Technology, Amity UniversityNoidaIndia

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