19 June 2017 Profiling and sorting Mangifera Indica morphology for quality attributes and grade standards using integrated image processing algorithms
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Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 1044314 (2017) https://doi.org/10.1117/12.2280751
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore
Abstract
Mango production is highly vital in the Philippines. It is very essential in the food industry as it is being used in markets and restaurants daily. The quality of mangoes can affect the income of a mango farmer, thus incorrect time of harvesting will result to loss of quality mangoes and income. Scientific farming is much needed nowadays together with new gadgets because wastage of mangoes increase annually due to uncouth quality. This research paper focuses on profiling and sorting of Mangifera Indica using image processing techniques and pattern recognition. The image of a mango is captured on a weekly basis from its early stage. In this study, the researchers monitor the growth and color transition of a mango for profiling purposes. Actual dimensions of the mango are determined through image conversion and determination of pixel and RGB values covered through MATLAB. A program is developed to determine the range of the maximum size of a standard ripe mango. Hue, light, saturation (HSL) correction is used in the filtering process to assure the exactness of RGB values of a mango subject. By pattern recognition technique, the program can determine if a mango is standard and ready to be exported.
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Jessie R. Balbin, Jessie R. Balbin, Janette C. Fausto, Janette C. Fausto, John Michael M. Janabajab, John Michael M. Janabajab, Daryl James L. Malicdem, Daryl James L. Malicdem, Reginald N. Marcelo, Reginald N. Marcelo, Jan Jeffrey Z. Santos, Jan Jeffrey Z. Santos, } "Profiling and sorting Mangifera Indica morphology for quality attributes and grade standards using integrated image processing algorithms ", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044314 (19 June 2017); doi: 10.1117/12.2280751; https://doi.org/10.1117/12.2280751
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