Image Recognition for Medicinal Plants Using Deep Neural Networks

B. Niketh Kumar; P. B. Shanthi

doi:10.1007/978-981-96-4008-9_45

Image Recognition for Medicinal Plants Using Deep Neural Networks

B. Niketh Kumar^*
, P. B. Shanthi

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Ayurvedic, herbal, and other conventional medicines all make the extensive use of medicinal plants. These medicinal plants are considered as one of the richest bio source of traditional plant medicine. It is difficult to identify medicinal plants from leaf images obtained from various sources. An automated system for medicinal plant classification based on its leaves using image processing and deep neural networks will play a significant role in the field of plant medicine. This study presents a novel method for classifying and identifying medicinal plants using deep neural network and image recognition. The models used in our study includes ResNet 50, VGG 16, and Inception V3 and performed classification task on medicinal plants. The accuracy rate of the ResNet model was highest among the model used and shown 85%. A comprehensive experiment on various models for medicinal plant recognition demonstrate the effectiveness for correctly classifying medicinal plants.

Original language	English
Title of host publication	Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024
Editors	Andrew Jeyabose, Andrew Jeyabose, Valentina Emilia Balas, Valentina Emilia Balas, Steven L. Fernandes
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	599-610
Number of pages	12
ISBN (Print)	9789819640072
DOIs	https://doi.org/10.1007/978-981-96-4008-9_45
Publication status	Published - 2025
Event	International Conference on Health Informatics, Intelligent Systems, and Networking Technologies, HINT 2024 - Manipal, India Duration: 13-03-2024 → 14-03-2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1286 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Health Informatics, Intelligent Systems, and Networking Technologies, HINT 2024
Country/Territory	India
City	Manipal
Period	13-03-24 → 14-03-24

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-96-4008-9_45

Cite this

Niketh Kumar, B., & Shanthi, P. B. (2025). Image Recognition for Medicinal Plants Using Deep Neural Networks. In A. Jeyabose, A. Jeyabose, V. E. Balas, V. E. Balas, & S. L. Fernandes (Eds.), Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024 (pp. 599-610). (Lecture Notes in Networks and Systems; Vol. 1286 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-4008-9_45

Niketh Kumar, B. ; Shanthi, P. B. / Image Recognition for Medicinal Plants Using Deep Neural Networks. Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. editor / Andrew Jeyabose ; Andrew Jeyabose ; Valentina Emilia Balas ; Valentina Emilia Balas ; Steven L. Fernandes. Springer Science and Business Media Deutschland GmbH, 2025. pp. 599-610 (Lecture Notes in Networks and Systems).

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title = "Image Recognition for Medicinal Plants Using Deep Neural Networks",

abstract = "Ayurvedic, herbal, and other conventional medicines all make the extensive use of medicinal plants. These medicinal plants are considered as one of the richest bio source of traditional plant medicine. It is difficult to identify medicinal plants from leaf images obtained from various sources. An automated system for medicinal plant classification based on its leaves using image processing and deep neural networks will play a significant role in the field of plant medicine. This study presents a novel method for classifying and identifying medicinal plants using deep neural network and image recognition. The models used in our study includes ResNet 50, VGG 16, and Inception V3 and performed classification task on medicinal plants. The accuracy rate of the ResNet model was highest among the model used and shown 85\%. A comprehensive experiment on various models for medicinal plant recognition demonstrate the effectiveness for correctly classifying medicinal plants.",

author = "\{Niketh Kumar\}, B. and Shanthi, \{P. B.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Health Informatics, Intelligent Systems, and Networking Technologies, HINT 2024 ; Conference date: 13-03-2024 Through 14-03-2024",

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Niketh Kumar, B & Shanthi, PB 2025, Image Recognition for Medicinal Plants Using Deep Neural Networks. in A Jeyabose, A Jeyabose, VE Balas, VE Balas & SL Fernandes (eds), Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. Lecture Notes in Networks and Systems, vol. 1286 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 599-610, International Conference on Health Informatics, Intelligent Systems, and Networking Technologies, HINT 2024, Manipal, India, 13-03-24. https://doi.org/10.1007/978-981-96-4008-9_45

Image Recognition for Medicinal Plants Using Deep Neural Networks. / Niketh Kumar, B.; Shanthi, P. B.
Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. ed. / Andrew Jeyabose; Andrew Jeyabose; Valentina Emilia Balas; Valentina Emilia Balas; Steven L. Fernandes. Springer Science and Business Media Deutschland GmbH, 2025. p. 599-610 (Lecture Notes in Networks and Systems; Vol. 1286 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Shanthi, P. B.

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PY - 2025

Y1 - 2025

N2 - Ayurvedic, herbal, and other conventional medicines all make the extensive use of medicinal plants. These medicinal plants are considered as one of the richest bio source of traditional plant medicine. It is difficult to identify medicinal plants from leaf images obtained from various sources. An automated system for medicinal plant classification based on its leaves using image processing and deep neural networks will play a significant role in the field of plant medicine. This study presents a novel method for classifying and identifying medicinal plants using deep neural network and image recognition. The models used in our study includes ResNet 50, VGG 16, and Inception V3 and performed classification task on medicinal plants. The accuracy rate of the ResNet model was highest among the model used and shown 85%. A comprehensive experiment on various models for medicinal plant recognition demonstrate the effectiveness for correctly classifying medicinal plants.

AB - Ayurvedic, herbal, and other conventional medicines all make the extensive use of medicinal plants. These medicinal plants are considered as one of the richest bio source of traditional plant medicine. It is difficult to identify medicinal plants from leaf images obtained from various sources. An automated system for medicinal plant classification based on its leaves using image processing and deep neural networks will play a significant role in the field of plant medicine. This study presents a novel method for classifying and identifying medicinal plants using deep neural network and image recognition. The models used in our study includes ResNet 50, VGG 16, and Inception V3 and performed classification task on medicinal plants. The accuracy rate of the ResNet model was highest among the model used and shown 85%. A comprehensive experiment on various models for medicinal plant recognition demonstrate the effectiveness for correctly classifying medicinal plants.

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BT - Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024

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Niketh Kumar B, Shanthi PB. Image Recognition for Medicinal Plants Using Deep Neural Networks. In Jeyabose A, Jeyabose A, Balas VE, Balas VE, Fernandes SL, editors, Advances in Health Informatics, Intelligent Systems, and Networking Technologies - Proceedings of HINT 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 599-610. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-4008-9_45