Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)

A Deep CNN-Based Approach Felicia Proposed for Identifying Medicinal and Edible Plants in the Western Ghats Region

Authors
Anagha Bharadwaj1, Srinidhi Kulkarni1, R. Bharath Kumar1, *
1Dept. of Computer Science and Engineering, Jyothy Institute of Technology, Bengaluru, India
*Corresponding author. Email: bharath.kr702@gmail.com
Corresponding Author
R. Bharath Kumar
Available Online 21 December 2023.
DOI
10.2991/978-94-6463-314-6_30How to use a DOI?
Keywords
Active Compounds; Ayurvedic Plants; Edible Plants; Felicia; Convolution Block; Classifier Block; Inception-V3
Abstract

Ayurvedic plants, which contain active compounds are used to treat various health conditions. On the other hand, edible plants contain essential nutrients that are required for our body’s proper functioning and can aid in preventing chronic diseases like diabetes, heart disease, and cancer. Numerous health benefits are obtained due to the inclusion of both.

In this study, we propose the implementation of the Felicia architecture (a comparison model based on a preexisting Convolution and Classifier block) enabling the identification of edible and ayurvedic plants. The suggested model takes advantage of Inception-V3, a cutting-edge deep neural network, to extract high-level information and improve plant identification accuracy.

The proposed approach can potentially be used in various fields, such as agriculture, botanical research, and medicine. It can also aid in the identification and classification of unknown plants, which can in turn help in detecting poisonous or harmful plants.

Overall, our research highlights the potential of using deep learning techniques for plant recognition tasks and provides a framework for future studies in this area.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
21 December 2023
ISBN
10.2991/978-94-6463-314-6_30
ISSN
2589-4900
DOI
10.2991/978-94-6463-314-6_30How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Anagha Bharadwaj
AU  - Srinidhi Kulkarni
AU  - R. Bharath Kumar
PY  - 2023
DA  - 2023/12/21
TI  - A Deep CNN-Based Approach Felicia Proposed for Identifying Medicinal and Edible Plants in the Western Ghats Region
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 295
EP  - 303
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_30
DO  - 10.2991/978-94-6463-314-6_30
ID  - Bharadwaj2023
ER  -