RICE QUALITY CLASSIFICATION USING ANDROID-BASED CONVOLUTIONAL NEURAL NETWORK
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Muhammad Yusuf(1)
University of Muhammadiyah Sorong
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Reinhard Ruimassa(2*)
University of Muhammadiyah Sorong
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Anisa Iriani Tawainella(3)
University of Muhammadiyah Sorong
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Devi Maharani(4)
University of Muhammadiyah Sorong
(*) Corresponding Author
Abstract
The importance of rice as a food commodity in Asia, especially in Indonesia, lies not only in its role as a major source of carbohydrates, but also in its quality that affects the selling value, nutritional aspects, and consumer satisfaction. In this context, research on rice quality becomes essential, with an emphasis on shape and color as the main factors affecting rice quality. Rice that is whiter, cleaner, and intact tends to have better quality, making the assessment of the level of whiteness and cleanliness of rice crucial in the food industry. To evaluate rice quality, this study utilizes Deep Learning technology with a focus on developing an Android application. The model used is the Convolutional Neural Network (CNN) with VGG-16 architecture. The application of the Extreme Programming development method in creating this application shows an adaptive and responsive approach to changing user needs. In essence, this application aims to provide a smart and efficient solution in classifying rice quality based on its physical characteristics. Through rice image analysis, the application can assess the level of whiteness and cleanliness, while the area analysis of the rice object is used to evaluate the level of integrity. Thus, this application is expected to be a tool that can help food industry players, farmers, and consumers in assessing and selecting rice that meets the desired quality standards. This research opens up opportunities to improve understanding related to rice quality evaluation by utilizing advances in Deep Learning technology.
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Copyright (c) 2024 Muhammad Yusuf, Reinhard Ruimassa, Anisa Iriani Tawainella, Devi Maharani
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