A Systematic Review of Medicinal Plant Identification Using Deep Learning

2024-07-16

Tran, Trien Phat

Ud Din, Fareed

Brankovic, Ljiljana

Sanin, Cesar

Hester, Susan M

Editor(s): Ngoc Thanh Nguyen, Richard Chbeir, Yannis Manolopoulos, Hamido Fujita, Tzung-Pei Hong, Le Minh Nguyen, Krystian Wojtkiewicz

Conference Publication

en

Publication

Springer Singapore

Singapore

Lecture Notes in Computer Science

10.1007/978-981-97-4985-0_1

une:1959.11/62075

Medicinal plants fulfil critical global health needs, but reliable identification poses barriers. This systematic review analyses 30 studies on deep learning for automated medicinal plant species classification to offer real-world insights. Convolutional neural networks (CNNs) demonstrate over 90% testing accuracy on plant organs such as leaves and flowers, enabling precise recognition models. While increasing species diversity and the use of crowdsourced data may pose performance challenges, optimisation strategies such as data augmentation and ensemble models may help mitigate accuracy declines. It appears that plant states (fresh vs. dry/sliced) may impact the model performance, although some models could distinguish maturity stages with sufficient data. Across geographical regions, CNNs show strong local identification capabilities, but generalised global models require larger, inclusive datasets. While mobile apps provide practical deployment avenues, robust mechanisms for continuous user-driven refinement are lacking. Ultimately, it appears that context-conscious deep learning approaches balancing efficiency and representation across diverse contexts are imperative for maximising real-world impact. This timely review consolidates evidence to guide the responsible development of specialised medicinal plant recognition systems.

link

Intelligent Information and Database Systems, p. 1-14

9789819749850

9789819749843

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14