Development of an Image Processing System for Defect Detection in Nam Dok Mai Golden Mangoes
DOI:
https://doi.org/10.55003/ETH.420208Keywords:
Image Processing, Deep Learning, Mask R-CNN, Defect Classification, Quality ControlAbstract
This study proposes an image processing-based approach for detecting surface defects in Nam Dok Mai mangoes. Each fruit was photographed from two sides to capture comprehensive defect characteristics. The images were subsequently converted into the HSV color space to highlight darker defect regions, such as brown or black, followed by morphological dilation to refine defect boundaries and facilitate accurate area measurement. Detected defects were quantified in square centimeters and categorized into four quality classes: Extra Class, Class I, and Class II, according to the Thai Agricultural Standard TAS 5-2567. Additionally, a fourth class, Bad Quality, was introduced to represent defects exceeding the Class II size threshold. The annotated dataset was prepared using Roboflow, where labeling and data augmentation were conducted to enhance sample diversity. The dataset was partitioned into a training set (80%) and a testing set (20%). While image processing techniques were employed for initial dataset preparation, the primary objective was to develop a Mask R-CNN model capable of autonomously detecting defects directly from raw images, thereby eliminating the reliance on manual preprocessing. Following the training phase, the Mask R-CNN model was evaluated for its ability to detect and classify mango defects. Experimental results demonstrated high Precision and F1-Score values, particularly in the Extra Class and Bad Quality groups. The model achieved an overall accuracy of 70.71%, reflecting its strong potential for real-world application. It is anticipated that this system could significantly improve the accuracy and efficiency of mango sorting processes in the agricultural sector, contributing to standardized and reliable quality control.
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