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Plant Science Today (PST)

Research Articles

Vol. 13 No. 1 (2026)

Medicinal plant leaf classification using an optimized multi-feature deep-learning approach

DOI
https://doi.org/10.14719/pst.12719
Submitted
13 November 2025
Published
29-01-2026 — Updated on 05-02-2026
Versions

Abstract

Plants are essential for human beings to survive on this planet earth. Numerous plant species exist out of which medicinal plants play an important role as they are used for a wide range of humanoid ailments. At least 80 % of individuals now use herbal supplements and medications for some part of primary healthcare, a huge increase in use over the previous 3 decades. Therefore, proper detection of medicinal plants is a challenging task. This paper hence proposes a framework that efficiently and effectively classifies medicinal plants. The proposed framework is divided into 2 stages: (i) image pre-processing and (ii) classification network. The former utilizes the Kuwahara filter and also introduces a novel Hybrid Whale Cat Optimization Model (HWCOM). The latter employs a deep-learning-based classification model to classify medicinal plant images. The proposed framework also leverages fused gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) features to automatically classify medicinal plants by using multi-feature extraction of leaf images.  Further, the proposed framework is trained and tested on medicinal plant dataset. The proposed framework can classify 30 different classes of medicinal plant leaves and provides an accuracy of 99.81 % when compared with other state-of-the-art.

References

  1. 1. Nawkar GM, Maibam P, Park JH, Sahi VP, Lee SY, Kang CH. UV-induced cell death in plants. Int J Mol Sci. 2013;14(1):1608-28. https://doi.org/10.3390/ijms14011608
  2. 2. Naserifar R, Bahmani M, Abdi J, Abbaszadeh S, Nourmohammadi GA, Rafieian-Kopaei M. A review of the most important native medicinal plants of Iran effective on leishmaniasis according to Iranian ethnobotanical references. Int J Adv Biotechnol Res. 2017;8:133-8.
  3. 3. Raskin I, Ribnicky DM, Komarnytsky S, Ilic N, Poulev A, Borisjuk N, et al. Plants and human health in the twenty-first century. Trends Biotechnol. 2002;20(12):522-31. https://doi.org/10.1016/S0167-7799(02)02080-2
  4. 4. Ganie SH, Upadhyay P, Das S, Sharma MP. Authentication of medicinal plants by DNA markers. Plant Gene. 2015;4:83-99. https://doi.org/10.1016/j.plgene.2015.10.002
  5. 5. Wirdiani NKA, Sudana AAKO. Medicinal plant recognition of leaf shape using localized arc pattern method. Int J Eng Technol. 2016;8(4):1847-54. https://doi.org/10.21817/ijet/2016/v8i4/160804167
  6. 6. Hu R, Lin C, Xu W, Liu Y, Long C. Ethnobotanical study on medicinal plants used by Mulam people in Guangxi, China. J Ethnobiol Ethnomed. 2020;16(1):40. https://doi.org/10.1186/s13002-020-00387-z
  7. 7. Singh V, Misra AK. Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric. 2017;4(1):41-9. https://doi.org/10.1016/j.inpa.2016.10.005
  8. 8. Wäldchen J, Mäder P. Plant species identification using computer vision techniques: a systematic literature review. Arch Comput Methods Eng. 2018;25(3):507-43. https://doi.org/10.1007/s11831-016-9206-z
  9. 9. Pferschy-Wenzig EM, Bauer R. The relevance of pharmacognosy in pharmacological research on herbal medicinal products. Epilepsy Behav. 2015;52(Pt B):344-52. https://doi.org/10.1016/j.yebeh.2015.10.014
  10. 10. Zhang F, Zhang X. Classification and quality evaluation of tobacco leaves based on image processing and fuzzy comprehensive evaluation. Sensors (Basel). 2011;11(3):2369-84. https://doi.org/10.3390/s110302369
  11. 11. Dahigaonkar TD, Kalyane R. Identification of Ayurvedic medicinal plants by image processing of leaf samples. Int Res J Eng Technol. 2018;5(2):351-5.
  12. 12. Sabarinathan C, Hota A, Raj A, Dubey VK, Ethirajulu V. Medicinal plant leaf recognition and show medicinal uses using convolutional neural network. Int J Glob Eng. 2018;1(1):120-7.
  13. 13. Khirade SD, Patil AB. Plant disease detection using image processing. In: Proceedings of the 2015 International Conference on Computing Communication Control and Automation (ICCUBEA); 2015; Pune, India. p. 768-71. https://doi.org/10.1109/ICCUBEA.2015.153
  14. 14. Bartyzel K. Adaptive Kuwahara filter. Signal Image Video Process. 2016;10(4):663-70. https://doi.org/10.1007/s11760-015-0791-3
  15. 15. Harakannanaver SS, Rudagi JM, Puranikmath VI, Siddiqua A, Pramodini R. Identification of Ayurvedic medicinal plants by image processing of leaf samples. Int Res J Eng Technol. 2022;3(5):305-10. https://doi.org/10.1016/j.gltp.2022.03.016
  16. 16. Dhingra G, Kumar V, Joshi HD. A novel computer vision-based neutrosophic approach for leaf disease identification and classification. Measurement. 2019;135:782-94. https://doi.org/10.1016/j.measurement.2018.11.055
  17. 17. Simion IM, Casoni D, Sârbu C. Classification of Romanian medicinal plant extracts according to the therapeutic effects using thin layer chromatography and robust chemometrics. J Pharm Biomed Anal. 2019;163:137-43. https://doi.org/10.1016/j.jpba.2018.12.010
  18. 18. Turkoglu M, Hanbay D. Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine. Phys A Stat Mech Appl. 2019;527:121297. https://doi.org/10.1016/j.physa.2019.121297
  19. 19. Qadri S, Qadri SF, Husnain M, Missen MMS, Khan DM, Muzammil-Ul-Rehman, et al. Machine vision approach for classification of citrus leaves using fused features. Int J Food Prop. 2019;22(1):2072-89. https://doi.org/10.1080/10942912.2019.1703738
  20. 20. Putri YA, Djamal EC, Ilyas R. Identification of medicinal plant leaves using convolutional neural network. J Phys Conf Ser. 2021;1845(1):012001. https://doi.org/10.1088/1742-6596/1845/1/012001
  21. 21. Haryono KA, Saleh A. A novel herbal leaf identification and authentication using deep learning neural network. In: Proceedings of the 2020 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM); 2020; Surabaya, Indonesia. p. 338-42. https://doi.org/10.1109/CENIM51130.2020.9297952
  22. 22. Nasiri A, Taheri-Garavand A, Fanourakis D, Zhang YD. Automated grapevine cultivar identification via leaf imaging and deep convolutional neural networks: a proof-of-concept study employing primary Iranian varieties. Plants (Basel). 2021;10(8):1628. https://doi.org/10.3390/plants10081628
  23. 23. Paulson A, Ravishankar S. AI-based indigenous medicinal plant identification. In: Proceedings of the Advanced Computing and Communication Technologies for High-Performance Applications (ACCTHPA); 2020; Cochin, India. p. 57-63. https://doi.org/10.1109/ACCTHPA49271.2020.9213224
  24. 24. Grinblat GL, Uzal LC, Larese MG, Granitto PM. Deep learning for plant identification using vein morphological patterns. Comput Electron Agric. 2016;127:418-24. https://doi.org/10.1016/j.compag.2016.07.003
  25. 25. Hu J, Chen Z, Yang M, Zhang R, Cui Y. A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Process Lett. 2018;25(6):853-7. https://doi.org/10.1109/LSP.2018.2809688
  26. 26. Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
  27. 27. Chu SC, Tsai PW, Pan JS. Cat swarm optimization. In: Trends in Artificial Intelligence. Lecture Notes in Computer Science. Berlin: Springer; 2006. p. 1-7. https://doi.org/10.1007/978-3-540-36668-3_94
  28. 28. Singh S, Srivastava D, Agarwal S. GLCM and its application in pattern recognition. In: Proceedings of the 5th International Symposium on Computational and Business Intelligence (ISCBI); 2017; Dubai, United Arab Emirates. p. 20-5. https://doi.org/10.1109/ISCBI.2017.8053537
  29. 29. Maćkiewicz A, Ratajczak W. Principal components analysis (PCA). Comput Geosci. 1993;19(3):303-42. https://doi.org/10.1016/0098-3004(93)90090-R
  30. 30. Roopashree S, Anitha J. Medicinal leaf dataset. Mendeley Data. 2020;V1.
  31. 31. Davis CC, Choisy P. Medicinal plants meet modern biodiversity science. Curr Biol. 2024;34(4):R158-73. https://doi.org/10.1016/j.cub.2023.12.038

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