
International Journal on Science and Technology
E-ISSN: 2229-7677
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Volume 16 Issue 2
2025
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Leaf Detection Using Image Processing
Author(s) | G. Sivathmika, U. Prathibha, G. Vandana, K. Swetha, R. Sreenivasulu |
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Country | India |
Abstract | The foundation of the medical system is made up of medicinal plants, which are the most abundant source of pharmaceutical intermediates, chemical entities for synthetic drugs, nutraceuticals, food supplements, folk remedies, and medications from traditional and modern medical systems. The therapeutic properties of these plants determine their classification. It is recognized that the classification of medicinal plants plays an important role in both the manufacturing of pharmaceuticals and the understanding of their applications in the medical field. Classifying medicinal plants according to their parts, such their leaves, has produced noteworthy outcomes. A machine learning and image processing-based automated system for identifying medicinal plants from their leaves has been introduced. The process of identifying medicinal plants using features taken from leaf photos and several pre-processing methods for feature extraction from a leaf are explained in this paper. Each leaf's length, width, perimeter, area, colour, rectangularity, and circularity were among the numerous characteristics that were taken out. It is anticipated that a web-based or mobile computer system for the automatic identification of medicinal plants will assist the community in expanding their knowledge of medicinal plants, assist taxonomists in creating more effective methods for identifying species, and play a major role in the production of pharmaceutical drugs. |
Field | Engineering |
Published In | Volume 16, Issue 2, April-June 2025 |
Published On | 2025-04-16 |
Cite This | Leaf Detection Using Image Processing - G. Sivathmika, U. Prathibha, G. Vandana, K. Swetha, R. Sreenivasulu - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3820 |
DOI | https://doi.org/10.71097/IJSAT.v16.i2.3820 |
Short DOI | https://doi.org/g9f2gs |
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IJSAT DOI prefix is
10.71097/IJSAT
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