Browsing by Author "Aran, Oya"

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    Cecid fly defect detection in mangoes using object detection frameworks
    (Springer Science and Business Media Deutschland GmbH, 2021) Baculo, Maria Jeseca C. an; Ruiz, Conrado; Aran, Oya
    Mango export has experienced rapid growth in global trade over the past few years, however, they are susceptible to surface defects that can affect their market value. This paper investigates the automated detection of a mango defect caused by cecid flies, which can affect a significant portion of the production yield. Object detection frameworks using CNN were used to localize and detect multiple defects present in a single mango image. This paper also proposes modified versions of R-CNN and FR-CNN replacing its region search algorithms with segmentation-based region extraction. A dataset consisting of 1329 cecid fly surface blemishes was used to train the object detection models. The results of the experiments show comparable performance between the modified and existing state-of-the-art object detection frameworks. Results show that Faster R-CNN achieved the highest average precision of 0.901 at aP50 while the Modified FR-CNN has the highest average precision of 0.723 at aP75.
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    Multi-task deep learning for automated tobacco leaf grading in a controlled environment
    (International Journal of Computer Theory and Engineering, 2026) Marzan, Charlie S.; Ruiz, Conrado Jr.; Aran, Oya
    Grading tobacco leaves is crucial for ensuring fair pricing and quality control, however, the process is still largely carried out manually, resulting in a slow, subjective, and often inconsistent outcome. In this work, we present a multi-task deep learning approach designed to automate the grading of air-cured Burley tobacco leaves in controlled settings. The model is constructed with shared convolutional layers and separate task-specific branches, allowing it to predict stalk group, quality, and color at the same time, in line with the hierarchical grading system. To improve consistency, images were preprocessed using coin-based size normalization, rotation alignment, and segmentation. In our experiments, the multi-task model with EfficientNetB0 achieved an accuracy of 94.82% and significantly outperformed the multi-class and single-task baselines, while reducing both training time and inference delay. These findings suggest that multi-task learning can be a valuable and robust method for automated tobacco grading, showing gains in accuracy, speed, and scalability compared to other algorithms.