IMAGE PROCESSING METHOD TO CHECK MATURITY INDEX OF ‘SEIN TA LONE’ MANGO IN MYANMAR
Keywords:
Color intensity values, Physical properties, Image processing, Mango maturity indexAbstract
Mango (Mangifera indica Linn., Family Anacardiaceae) plays a central role as a favorite tropical fruit crop every year in Myanmar. The objective was to assess the maturity indices of ‘Sein Ta Lone’ mango with classification of fruit skin color and quality attributes of the mango to generate recommendations. The experiment was conducted on ‘Sein Ta Lone’ mangoes in Myanmar and arranged with 2×3 factorial experiment in randomized complete block design with five replications. Week of harvest was made as block and as treatments, ‘mangoes from three different levels-low, medium and high with bagging and unbagging’ were arranged. The collected original images were analyzed by converting to grey-scale images and binary images with Python Language Programming. In image analysis, the color intensity values and size values in pixels were received from the background subtraction process. The change of color intensity value and its physical size from three weeks of bagged and unbagged mangoes were clarified. Identifying parameters to be used for identifying maturity and mango type were revealed via result obtained from analysis of variance. The best color parameters Blue and average of Red, Green and Blue were employed for classifying fruit maturity with K-nearest neighbors (KNN) classifier. For three weeks of harvest, 88.89% accuracy for color intensity B value of bagged mango of same trees and 100.00% accuracy for unbagged mango were achieved and they yielded the highest accuracy, while considering 77.78% accuracy for average RGB value of bagged mango and 88.89% accuracy for unbagged mango.
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