Advertisement

A Computer Vision Approach for Jackfruit Disease Recognition

  • Md. Tarek Habib
  • Md. Robel Mia
  • Md. Jueal MiaEmail author
  • Mohammad Shorif Uddin
  • Farruk Ahmed
Conference paper
  • 28 Downloads
Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

Bangladesh extensively depends on agriculture in terms of economy as well as food security for its huge population. For this reason, it is very important to efficiently grow a plant and enhance its yield. Quantity and quality of fruits can degrade due to various diseases that are very much crucial issues. A little research has been conducted for recognition of jackfruit disease to help distant farmers, utmost of who need proper cultivation support. Recognition of jackfruit diseases poses two challenging problems, i.e., detection of disease and classification of disease. In this research, we perform an in-depth investigation of an online automated agro-medical expert system that processes an image captured with handheld devices or mobile phones and recognizes the diseases for helping the distant farmers. Adequate experiment has been performed to prove the efficiency of our proposed system. k-means clustering algorithm is used to extract discriminatory features from segmented out images of diseased jackfruits. After that, we classify the diseases using support vector machines (SVMs). Our classification accuracy is nearly about 90%, which seems to be reliable as well as ensuring by comparing performances with the relevant works.

Keywords

Jackfruit disease Agro-medical expert system Discriminatory features K-means clustering Support vector machines (SVMs) 

References

  1. 1.
    Bangladesh: Employment in agriculture (2019). https://www.theglobaleconomy.com/Bangladesh/Employment_in_agriculture. Accessed 17 May 2019
  2. 2.
    Bangladesh: GDP share of agriculture (2019). https://www.theglobaleconomy.com/Bangladesh/Share_of_agriculture. Accessed 17 May 2019
  3. 3.
    Jackfruit (2019). http://en.banglapedia.org/index.php?title=Jackfruit. Accessed 20 May 2019
  4. 4.
    Rahman MA, Afroz M (2016) Survey on the diseases of Jackfruit and some aspects of control measures for Gummosis disease in Bangladesh. Eco-Friendly Agric J 9(2):10–14Google Scholar
  5. 5.
    Haq N (2006) “Jackfruit: artocarpus heterophyllus. Crops for the future, vol 10Google Scholar
  6. 6.
    Habib MT, Majumder A, Jakaria AZM, Akter M, Uddin MS, Ahmed F (2018) Machine vision based papaya disease recognition. J King Saud Univ—Comput Inf Sci.  https://doi.org/10.1016/j.jksuci.2018.06.006CrossRefGoogle Scholar
  7. 7.
    Samajpati BJ, Degadwala SD (2016) Hybrid approach for apple fruit diseases detection and classification using random forest classifier. In: 2016 international conference on communication and signal processing (ICCSP), Melmaruvathur, pp 1015–1019Google Scholar
  8. 8.
    Habib MT, Majumder A, Nandi RN, Uddin MS, Ahmed F (2018) A comparative study of classifiers in the context of Papaya disease recognition. In: Proceedings of international joint conference on computational intelligence (IJCCI)Google Scholar
  9. 9.
    Kumar YHS, Suhas G (2016) Identification and classification of fruit diseases. In: Proceedings of the recent trends in image processing and pattern recognition (RTIP2R), India, 16–17 Dec 2016, pp 382–390Google Scholar
  10. 10.
    Chopaade PB, Bhagyashri K (2016) Image processing based detection and classification of leaf disease on fruits crops. In: Proceedings of the 3rd national conference on advancements in communication, computing and electronics technology (ACCET-2016), India, 11–12 Feb 2016Google Scholar
  11. 11.
    Rozario LJ, Rahman T, Uddin MS (2016) Segmentation of the region of defects in fruits and vegetables. Int J Comput Sci Inf Secur 14(5):399–406Google Scholar
  12. 12.
    Hosen MI, Tabassum T, Akhter J, Islam MI (2018) Detection of fruits defects using colour segmentation technique. Int J Comput Sci Inf Secur 16(6):215–223Google Scholar
  13. 13.
    Batule VB, Chavan GU, Sanap VP, Wadkar KD (2016) Leaf disease detection using image processing and support vector machine (SVM). J Res 02(02):74–77Google Scholar
  14. 14.
    Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-WesleyGoogle Scholar
  15. 15.
    Habib MT, Rokonuzzaman M (2011) Distinguishing feature selection for fabric defect classification using neural network. J Multimed 6(5):416–424CrossRefGoogle Scholar
  16. 16.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621CrossRefGoogle Scholar
  17. 17.
    Confusion Matrix (2019). https://en.wikipedia.org/wiki/Confusion_matrix. Accessed 5 June 2019

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJahangirnagar UniversityDhakaBangladesh
  2. 2.Department of Computer Science and EngineeringDaffodil International UniversityDhakaBangladesh
  3. 3.Department of Computer Science and EngineeringIndependent University, BangladeshDhakaBangladesh

Personalised recommendations