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  4. Eng-ECE-M-Thesis
Please use this identifier to cite or link to this item: https://rsuir-library.rsu.ac.th/handle/123456789/3163
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dc.contributor.advisorRong Phoophuangpairoj-
dc.contributor.authorXiangchen Dong-
dc.date.accessioned2026-01-28T02:48:21Z-
dc.date.available2026-01-28T02:48:21Z-
dc.date.issued2024-
dc.identifier.urihttps://rsuir-library.rsu.ac.th/handle/123456789/3163-
dc.descriptionThesis (M. Eng. (Electrical and Computer Engineering)) -- Rangsit University, 2024en_US
dc.language.isoenen_US
dc.publisherRangsit University. Libraryen_US
dc.subjectMachine learningen_US
dc.subjectMango -- Temperatureen_US
dc.subjectDeep learning (Machine learning) -- Industrial applicationsen_US
dc.titleMango maturity classification using machine learningen_US
dc.typeThesisen_US
dc.description.other-abstractMangoes, predominantly cultivated in tropical and subtropical regions, are cherished for their sweet and sour taste, pleasant aroma, and rich vitamin content. This study focused on classifying mango ripeness using various machine learning classifiers. Images of mangoes at different ripeness stages were collected and used to train classifiers, including Convolutional Neural Network (CNN), MobileNet, ResNet50, and VGG16. The experimental results demonstrated that CNN, MobileNet, ResNet50, and VGG16 achieved accuracies of 81.30%, 85.11%, 73.66%, and 90.08%, respectively. VGG16 attained the highest classification accuracy, with classification accuracies from class 0 to class 5 being 98.85%, 98.85%, 95.80%, 95.80%, 95.42%, and 95.42%, respectively. Building on these results, the second part of the study utilized the top-performing VGG16 model to output softmax layer data from mango images. This softmax data, combined with the mangoes’ RGB and Lab color data, was used to make predictions via linear regression and artificial neural networks (ANNs). The comparative analysis revealed that ANN generally yielded better predictive performanceen_US
dc.description.degree-nameMaster of Engineeringen_US
dc.description.degree-levelMaster's Degreeen_US
dc.contributor.degree-disciplineElectrical and Computer Engineeringen_US
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