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AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

Mohd Asif Hajam1, Tasleem Arif1, Akib Mohi Ud Din Khanday2, Mudasir Ahmad Wani3,*, Muhammad Asim3,*

1 Department of Information Technology, Baba Ghulam Shah Badshah University, Rajouri, Jammu and Kashmir, 185234, India
2 Department of Computer Science, Samarkand International University of Technology, Samarkand, 141500, Uzbekistan
3 EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

* Corresponding Authors: Mudasir Ahmad Wani. Email: email; Muhammad Asim. Email: email

(This article belongs to the Special Issue: Advances in Pattern Recognition Applications)

Computers, Materials & Continua 2024, 81(2), 2077-2131. https://doi.org/10.32604/cmc.2024.057136

Abstract

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification, drawing insights from literature across renowned repositories. This paper critically summarizes relevant literature based on AI algorithms, extracted features, and results achieved. Additionally, it analyzes extensively used datasets in automated plant classification research. It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition. Moreover, this rigorous review study discusses opportunities and challenges in employing these AI-based approaches. Furthermore, in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions. This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

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Cite This Article

APA Style
Hajam, M.A., Arif, T., Khanday, A.M.U.D., Wani, M.A., Asim, M. (2024). Ai-driven pattern recognition in medicinal plants: A comprehensive review and comparative analysis. Computers, Materials & Continua, 81(2), 2077-2131. https://doi.org/10.32604/cmc.2024.057136
Vancouver Style
Hajam MA, Arif T, Khanday AMUD, Wani MA, Asim M. Ai-driven pattern recognition in medicinal plants: A comprehensive review and comparative analysis. Comput Mater Contin. 2024;81(2):2077-2131 https://doi.org/10.32604/cmc.2024.057136
IEEE Style
M.A. Hajam, T. Arif, A.M.U.D. Khanday, M.A. Wani, and M. Asim, “AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis,” Comput. Mater. Contin., vol. 81, no. 2, pp. 2077-2131, 2024. https://doi.org/10.32604/cmc.2024.057136



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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