Segmentation of Red Onion Leaf Images Using Otsu's Thresholding Method
Abstract
The segmentation of red onion leaf images using the Otsu method has been the focus of research in image processing. This study aims to apply the Otsu method to segment red onion leaf images and provide a brief overview of the obtained results. This method automatically determines the optimal threshold value based on image analysis. The method is applied to a dataset of red onion leaf images with significant variations in intensity, background, and brightness levels. The segmentation results are evaluated using evaluation metrics such as Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The study uses two test scenarios with bright and dark backgrounds to determine the best segmentation results. The research findings show that the Otsu thresholding method provides good segmentation results with an average PSNR of 49.34744 dB and MSE of 0.85681. The best results are obtained using red onion leaf images with a dark sandy background. It is expected that this research will contribute to image processing and object segmentation in agricultural applications and related studies.
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