NON-DESTRUCTIVE CLASSIFICATION OF FRUITS BASED ON COLOR BY USING MACHINE LEARNING TECHNIQUES

Authors

  • Dr.Kedri Janardhana, Dr.V. Elizabeth Jesi, M.Vijayaragavan, Rekha Baghel Dr.A.Nirmal Kumar

Abstract

 Inclusion of computer vision and image processing technique in agriculture field providing a user friendly environment in quality testing and grading of fruit before placing it in market. Automated quality testing and grading of fruits influenced with the extracted quality parameters of fruit image and number of dataset used in training phase of machine. Color, size, texture and surface defect are the basic parameters in quality measure of fruits in agriculture field. This paper focused on classification accuracy based on extracted color and geometric features of fruit mango (magniferia Indica) and used sample size in training phase of machine learning algorithm. In this study maturity of mango is predicted with extracted color features by using a combination of RGB, HSI, HSV color model and classification is done using  Naïvebayes and BPNN machine learning algorithms. This complete method passes from three phases 1) image pre-processing 2) feature extraction and 3) classification. The experimental results illustrate the usefulness of these measures by providing the prior information in classification. BPNN results are satisfactory in both cases of used features like i) color features ii) color and geometric features.

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Published

2021-03-23 — Updated on 2021-06-07

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How to Cite

Dr.Kedri Janardhana, Dr.V. Elizabeth Jesi, M.Vijayaragavan, Rekha Baghel Dr.A.Nirmal Kumar. (2021). NON-DESTRUCTIVE CLASSIFICATION OF FRUITS BASED ON COLOR BY USING MACHINE LEARNING TECHNIQUES. International Journal of Modern Agriculture, 10(1), 1057 - 1069. Retrieved from http://modern-journals.com/index.php/ijma/article/view/714 (Original work published March 23, 2021)

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Articles