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Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer

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Abstract

In machine learning, image classification accuracy generally depends on image segmentation and feature extraction methods with the extracted features and its qualities. The main focus of this paper is to determine the defected area of mangoes using image segmentation algorithm for improving the classification accuracy. The Enhanced Fuzzy based K-means clustering algorithm is designed for increasing the efficiency of segmentation. Proposed segmentation method is compared with K-means and Fuzzy C-means clustering methods. The geometric, texture and colour based features are used in the feature extraction. Process of feature selection is done by Maximally Correlated Principal Component Analysis (MCPCA). Finally, in the classification step, severe portions of the affected area are analyzed by Backpropagation Based Discriminant Classifier (BBDC). Proposed classifier is compared with BPNN and Naive Bayes classifiers. The images are classified into three classes in final output like Class A –good quality mango, Class B-average quality mango, and Class C-poor quality mango. Finally, the evaluated results of the proposed model examine various defected and healthy mango images and prove that the proposed method has the highest accuracy when compared with existing methods.

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Authors and Affiliations

  1. U.K. Technical University, Dehradun, Uttarakhand, India

    Neeraj Kumari

  2. Birla Institute, Bhimtal, India

    Ashutosh Kr. Bhatt

  3. CCSIT,TMU, Moradabad, India

    Rakesh Kr. Dwivedi

  4. Amrapali Institute Haldwani, Haldwani, India

    Rajendra Belwal

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  1. Neeraj Kumari
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  2. Ashutosh Kr. Bhatt
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  3. Rakesh Kr. Dwivedi
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  4. Rajendra Belwal
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Correspondence to Neeraj Kumari.

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Kumari, N., Kr. Bhatt, A., Kr. Dwivedi, R. et al. Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer. Multimed Tools Appl 80, 4943–4973 (2021). https://doi.org/10.1007/s11042-020-09747-z

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  • DOI: https://doi.org/10.1007/s11042-020-09747-z

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