Comparison of LoG (Laplacian of Gaussians) and DoG (Difference of Gaussians) Algorithms in the Measurement of the Nerve Quality Level of Gotu Gotu Leaves

Aulia Aulia putri; Rahmadani; I Gusti Prahmana

doi:10.59934/jaiea.v5i1.1670

Authors

Aulia Aulia putri Stmik Kaputama
Rahmadani STMIK KAPUTAMA
I Gusti Prahmana STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v5i1.1670

Keywords:

Centella asiatica; Digital Image Processing; Edge Detection; Laplacian of Gaussian; Difference of Gaussian

Abstract

Gotu gotu leaves (Centella asiatica) are herbal plants with high pharmacological benefits thanks to the content of active compounds such as asiaticoside, madecassoside, and asiatic acid. The pharmacological quality of these leaves is greatly influenced by the condition of their neural networks. This study aims to compare two popular edge detection algorithms, namely Laplacian of Gaussian (LoG) and Difference of Gaussian (DoG), in measuring the level of nerve quality of the leaves of the peg. The method used is Python-based digital image processing with OpenCV, using primary data in the form of gotu tu leaf images in JPEG, PNG, and JPG formats. The results showed that the DoG algorithm was more efficient in computational time and was able to produce sharper leaf neural images, while LoG excelled at detecting the main line but tended to generate over-detection. Thus, the DoG is more suitable for the implementation of an automated system for assessing the quality of gotu gotu leaves in agriculture and pharmaceuticals.

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