Image Segmentation of Red Onion Leaves Using Canny Edge Detection Method
DOI:
https://doi.org/10.29407/noe.v7i01.20558Keywords:
Daun Bawang Merah, Segmentasi, Canny Edge DetectionAbstract
One important plant in agriculture is red onion, and red onion leaves are often observed to evaluate their health and development. This research aims to develop a method for segmenting images of red onion leaves using the Canny edge detection method. The Canny edge detection method is well-known for its ability to accurately and sharply detect edges in images. This method automatically determines optimal values based on image analysis. It is applied to a dataset of red onion leaf images that have significant variations in intensity, background, and brightness levels. The segmentation results are evaluated using evaluation metrics such as MSE and PSNR. The research findings indicate that the Otsu thresholding method provides good segmentation results with a PSNR of 51.4891 and MSE of 0.4590. This research is expected to broaden the understanding of image analysis in the context of agriculture and provide support for the development of more efficient and sustainable agricultural technologies.
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