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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bangladesh: Employment in agriculture (2019). https://www.theglobaleconomy.com/Bangladesh/Employment_in_agriculture. Accessed 17 May 2019
Bangladesh: GDP share of agriculture (2019). https://www.theglobaleconomy.com/Bangladesh/Share_of_agriculture. Accessed 17 May 2019
Jackfruit (2019). http://en.banglapedia.org/index.php?title=Jackfruit. Accessed 20 May 2019
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):10–14
Haq N (2006) “Jackfruit: artocarpus heterophyllus. Crops for the future, vol 10
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.2018.06.006
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 1015–1019
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)
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 382–390
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 2016
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):399–406
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):215–223
Batule VB, Chavan GU, Sanap VP, Wadkar KD (2016) Leaf disease detection using image processing and support vector machine (SVM). J Res 02(02):74–77
Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley
Habib MT, Rokonuzzaman M (2011) Distinguishing feature selection for fabric defect classification using neural network. J Multimed 6(5):416–424
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621
Confusion Matrix (2019). https://en.wikipedia.org/wiki/Confusion_matrix. Accessed 5 June 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Habib, M., Mia, M., Mia, M., Uddin, M., Ahmed, F. (2020). A Computer Vision Approach for Jackfruit Disease Recognition. 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_28
Download citation
DOI: https://doi.org/10.1007/978-981-15-3607-6_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3606-9
Online ISBN: 978-981-15-3607-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)