This research aims to develop a method for improving the process of separating partially rotten fruit of ripe mango to the process of creating new creative Mango Stir-Fry. The partial rotten approximation process was based on image processing using Gaussian Mixture Model, HSV color model, dilation, erosion, Otsu’s binarization thresholding, and image color proportions techniques for mobile application development. The results showed that the techniques presented in this study could sort and approximate partially rotten mangoes with an accuracy of 99.06%. Moreover, the developed mobile application can approximate the net weight of ripe mango pulp and the net weight of Mango Stir-Fry to be obtained. These approximation errors were 0.68% and 1.91% for the net weight of net ripe mango pulp and Mango Stir-Fry, respectively. Therefore, this work helps the cooking process to classify partially rotten ripe mangoes and estimate the volume of processed products more conveniently.
This research aims to create value from vegetable scraps of the Thai vegetable carving process for cooking Thai desserts based on the original six colors of vegetables using the ontology technique. The developed multiple-ontology between the classes of vegetables, Thai vegetable carvings, and Thai desserts can be linked to related data, including images, videos, and websites. The results showed that multipleontology developed with an efficiency of 99.99% accuracy. In addition, when referred to the ten experts for evaluation under criteria such as completeness, adaptability, clarity, and consistency, the result showed that the mean was 4.98, and the standard deviation was 0.16.
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