Abstract

Mangos are an important agricultural commodity in the global market for fresh products. In Myanmar, the type of mango called SeinTaLone is the best taste and the most people like it. Another type of mango called MaSawYin is not good taste but it is visually similar to the SeinTaLone. So, some people are difficult to classify the mango varieties. A means for distinguishing mango varieties is needed and therefore, some reliable technique is needed to discriminate varieties rapidly and non destructively. The main objective of this research was to classify the varieties of mango fruit that occur in Myanmar using Naive Bayes algorithm. The methodology involved image acquisition, pre processing and segmentation, feature extraction and classification of mango varieties. A method for classifying varieties of mangos using image processing technique is proposed in this paper. RGB image was first converted to HSV image. Then by using edge detection method and morphological operation, region of interest was segmented by taking into account only the HUE component image of the HSV image. Later, a total of 4 shape features and 13 texture features were extracted. Extracted features were given as inputs to a Naive Byaesian classifier to classify the test images as each type. The data set used had 50 mango images for each varieties of mango for training and 20 images of mango for each variety for testing. Ohnmar Win "Classification of Mango Fruit Varieties using Naive Bayes Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26677.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26677/classification-of-mango-fruit-varieties-using-naive-bayes-algorithm/ohnmar-win

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