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- W4387444322 abstract "Pomelo (Citrus grandis), the largest among citrus fruits, is a signature product in the Philippines' Davao region. Determining the quality of this high-value fruit is a complex task that requires keen observation skills and experience. This intricate process, however, could be simplified by the growing applications of machine learning. Hence, the study proposes a non-invasive pomelo grading system mainly by image processing. The classification process is modeled through a multiclass support vector machine that will categorize pomelos into three classes. Samples beyond these groups will be considered rejects and are not qualified for marketing. The process will utilize four digital cameras to capture images of samples in all directions. These images will be processed to obtain the sample's coloration, percentage of surface defect, texture, size, and shape. The system will also use a microcontroller-based load sensor to measure a pomelo's weight. Conducting these procedures attempt to emulate the conventional method of pomelo grading. The extracted features will serve as input to the machine learning model that was developed using Matlab's Classification Learner App and trained using 360 prelabeled pomelo samples. Results indicate a promising accuracy of 96.67% for predicting a pomelo class which suggests the viability of classifying pomelos using their external features through machine learning." @default.
- W4387444322 created "2023-10-10" @default.
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- W4387444322 date "2023-07-27" @default.
- W4387444322 modified "2023-10-11" @default.
- W4387444322 title "A Non-Invasive Computer-Based Pomelo Grade Classifier Using Multiclass Support Vector Machine" @default.
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- W4387444322 doi "https://doi.org/10.1109/icivc58118.2023.10269861" @default.
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