CNN-based Indian medicinal leaf type identification and medical use recommendation

Title

CNN-based Indian medicinal leaf type identification and medical use recommendation

Creator

Praveena S.; Pavithra S.M.; Kumar A.D.V.; Veeresha P.

Description

Medicinal leaves are playing a vital role in our everyday life. There are an enormous amount of species present in the world. Identification of each type would be a tedious task. Using image processing technology, we can overcome this problem by providing computer vision with the help of a convolution neural network (CNN). The objective of this research is to find out the best CNN model that helps in classifying the plant leaf species and identifying its category. In this research work, the proposed basic CNN model consisting of four convolution layers uses ten different medicinal leaf species each belonging to two categories providing an accuracy of 96.88%. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

Source

Neural Computing and Applications, Vol-36, No. 10, pp. 5399-5412.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Confusion matrix; Convolution neural network; Gradio; Graphical user interface

Coverage

Praveena S., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Pavithra S.M., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Kumar A.D.V., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, 560029, India; Veeresha P., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 9410643

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s00521-023-09352-9

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181231337&doi=10.1007%2fs00521-023-09352-9&partnerID=40&md5=7bd41c2ae572b0138d179435a4809b86