Skip to main content
SpringerLink
Log in

Recognition and Classification of Fruit Diseases Based on the Decomposition of Color Wavelet and Higher-Order Statistical Texture Features

  • Conference paper
  • First Online:
Proceedings of International Joint Conference on Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

  • 538 Accesses

Abstract

Most of the circumstances, it is to be considered that the forerunners of economic losses and production in the agriculture industry of a country are diseases of fruit. To condense the probability of such losses, early detection and removal of fruit diseases can be cogitated. Manual identification of defected fruit in the fruit surface is carried out manually employing human inspection which is very time consuming and laborious. Computer-aided automatic fruit disease recognition and classification system can reduce the cognitive burden of the traditional system and improve its efficacy of false revealing of defected fruit. In this paper, we have proposed a framework for fruit disease identification using segmentation techniques and supervised learning based on data analyzed from the decomposition of color wavelet and higher-order statistical texture feature. Three types of common apple diseases are taken into considerations in this paper. We evaluated our method that can segment the image and shows its output as a result of classified defected fruit. As there are many popular techniques for image segmentation, we have used k-means clustering because of its unsupervised learning mechanism and it can be used to segment the region of interest from the background. Segmented images are extracted from the decomposition of color wavelet with higher-order statistical texture feature and combined which are used to train a multi-class linear support vector machine (MSVM). Gray-level run-length matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ = 0°, 45°, 90°, 135°). The experimental result demonstrates that the proposed approach is auspicious which triumphs the best performance of accuracy of 94.95% for the classification of defected fruit.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Roberts MJ, Schimmelpfennig D, Ashley E, Livingston M, Ash M, Vasavada U (2006) The value of plant disease early-warning systems (No 18). Economic research service, United States department of agriculture

    Google Scholar 

  2. Mohana SH, Prabhakar CJ, and Praveen Kumar PU. Surface defect detection and grading of apples. In: Proceedings of the international conference on multimedia processing, communication and information technology MPCIT

    Google Scholar 

  3. Zhang YJ, Li MZ, Qiao J, Liu G (2008) Segmentation algorithm for apple recognition using image features and artificial neural network. Acta Opt Sin 28:2104–2108

    Article  Google Scholar 

  4. Lak MB, Minaei S, Amiriparian J, Beheshti B (2010) Apple fruits recognition under natural luminance using machine vision. Adv J Food Sci Technol 2:325–327

    Google Scholar 

  5. Raihana A, Sudha R (2016) Defect detection and classification of apple fruit images using the modified watershed segmentation method. IJSTE Int J Sci Technol Eng 3(6)

    Google Scholar 

  6. Kleynen O, Leemans V, Destain MF (2005) Development of a multi-spectral vision system for the detection of defects on apples. J Food Eng 69:41–49

    Article  Google Scholar 

  7. Leemans V, Magein H, Destain MF (1999) Defect Segmentation on ‘Jonagold‘ Apples using Color Vision and a Bayesian Classification Method. Comput Electron Agric 23:43–53

    Article  Google Scholar 

  8. Leemans V, Magein H, Destain MF (1998) Defect segmentation on ‘Golden Delicious’ apples by using color machine vision. Comput Electron Agric 20:117–130

    Article  Google Scholar 

  9. YuzhenLua RenfuLub (2018) Fast Bi-dimensional empirical mode decomposition as an image enhancement technique for fruit defect detection. Comput Electron Agric 152:314–323

    Article  Google Scholar 

  10. YuzhenLua RenfuLub (2017) Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging. Biosys Eng 160:30–41

    Article  Google Scholar 

  11. Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM, Int J Sci Res Publ 3(5). ISSN 2250-3153 (1 May 2013)

    Google Scholar 

  12. David JF, Yau KY, Elmagarmid AK (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process (TIP) 10(10):1454–1466

    Article  Google Scholar 

  13. Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell (PAMI) 6(6):641–647

    Article  Google Scholar 

  14. Shih FY, Cheng S (2005) Automatic seeded region growing for color image segmentation. Image Vis Comput (IVC) 23(10):877–886

    Article  Google Scholar 

  15. Dubey SR, Jalal AS (2012) Robust approach for fruit and vegetable classification. Int Conf Model Optim Comput Procedia Eng 38:3449–3453

    Google Scholar 

  16. Dubey SR, Jalal AS (2013) Species and variety detection of fruits and vegetables from images. Int J Appl Pattern Recognit (IJAPR) 1(1):108–126

    Article  Google Scholar 

  17. Gonzalez R, Woods R (2007) Digital Image Processing, 3rd edn. Prentice-Hall

    Google Scholar 

  18. Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. In: Eleventh international multi-conference on information processing-2015 (IMCIP-2015). Procedia Comput Sci 54:764–771

    Google Scholar 

  19. Abdul-Nasir AN, Mashor MY, Mohamed Z. Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering. WSEAS transactions on biology and biomedicine

    Google Scholar 

  20. Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M (2003) Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomed 7(3):141–152

    Article  Google Scholar 

  21. Van deWouwer G, Scheunders P, Van Dyck D (1999) Statistical texture characterization from discrete wavelet representations. IEEE Trans Image Process 8:592–598

    Article  Google Scholar 

  22. Galloway MM (1975) Texture analysis using gray level runs lengths. Comput Graph Image Process 4:172–179

    Article  Google Scholar 

  23. Chu A, Sehgal CMA, Greenleaf JF (1990) Use of gray value distribution of run lengths for texture analysis. Pattern Recogn Lett 11:415–419

    Article  Google Scholar 

  24. Belur VD, Holder EB (1981) Image characterizations based on joint gray level-run length distributions. Pattern Recognit Lett 12:497–502

    Google Scholar 

  25. Rocha A, Hauagge DC, Goldenstein JWS. Automatic produce classification from images using, texture and appearance cues. In: XXI Brazilian symposium on computer graphics and image processing

    Google Scholar 

  26. Gatica Gabriel, Best Stanley, Ceroni José, Lefranc Gastón (2013) Olive fruits recognition using neural networks, information technology and quantitative management (ITQM2013). Procedia Comput Sci 17:412–419

    Article  Google Scholar 

  27. Mayannavar SM, Sangani SP, Gollagi SG, Huddar MG, Pawar MR (2016) Adaptive neuro-fuzzy inference system for recognition of cotton leaf diseases. Int J Sci Dev Res (IJSDR) 1(8). (August 2016 IJSDR)

    Google Scholar 

  28. Shafi ASM, Rahman MB, Rahman MM (2018) Fruit disease recognition and automatic classification using MSVM with multiple features. Int J Comput Appl 181(10):0975–8887. (August 2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Khwaja Yunus Ali University, Enayetpur, 6751, Sirajgonj, Bangladesh

    A. S. M. Shafi

  2. Mawlana Bhashani Science and Technology University, Santosh, 1902, Tangail, Bangladesh

    Mohammad Motiur Rahman

Authors
  1. A. S. M. Shafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Motiur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. S. M. Shafi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shafi, A.S.M., Rahman, M.M. (2020). Recognition and Classification of Fruit Diseases Based on the Decomposition of Color Wavelet and Higher-Order Statistical Texture Features. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3607-6_6

Download citation

Publish with us

Policies and ethics

Search

Navigation