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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM38478

Ayurvedic Plant Detection Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM38478

Volume:

8 |

11 |

Nov 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VNM

Veera Nagaiah Maddikayala

HA

Harsh Agarwal

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Abstract

Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. In this project we explore feature vectors from both the front and back side of a green leaf along with morphological features to arrive at a unique optimum combination of features that maximizes the identification rate. A database of medicinal plant leaves is created from scanned images of front and back side of leaves of commonly used medicinal plants. The leaves are classified based on the shape and dimension combination. It is expected that for the automatic identification of medicinal plants this system will help the community people to develop their knowledge on medicinal plants, help taxonomists to develop more efficient species identification techniques and also participate significantly in the pharmaceutical drug manufacturing.

KEYWORDS

Classification feature extraction morphological features optimization plant identification texture features

Cite This Article

Veera Nagaiah Maddikayala, Harsh Agarwal, "Ayurvedic Plant Detection Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 11, Nov 2024.

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Publication Date

11/10/2024

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The authors declare no conflict of interest related to this research work.

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