Skip to content
2000
Volume 4, Issue 1
  • ISSN: 2950-3779
  • E-ISSN: 2950-3787

Abstract

Introduction

Pharmaceutical companies widely use herbals as the main ingredients in formulations. Nevertheless, conventional techniques for herbal identification, like morphological and microscopic identification, are labor-based, require expertise, and are time-consuming. These challenges hamper the identification and quality control of herbals.

Objective

This review explores the utilization of a computer-based model in the recognition and quality control of herbals and their adulterants. The study highlights artificial intelligence's power to transform the herbal industry by improving quality control, therapeutic reproducibility, and stakeholder accessibility.

Methods

The paper examines recent advanced computational methods for identifying herbals. Artificial intelligence addresses issues such as data complications and adulterant recognition. The paper also compares artificial intelligence-based methods with traditional approaches, focusing on their benefits in speed, cost-effectiveness, and precision.

Results and Discussion

Artificial intelligence methods show significant power in the herbal field. Their use improves herbal recognition, adulterant discovery, and quality assurance by leveraging data-driven algorithms. Moreover, artificial intelligence decreases laboratory expenses and increases the convenience and suitability of herbals.

Conclusion

Artificial intelligence provides transformative solutions for the herbal industry by addressing venerable challenges in herbal identification and adulterant detection. Its interdisciplinary approach promises better regularity, increased remedial outcomes, and increased trust of consumers.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cucs/10.2174/0129503779378012250613194901
2025-06-20
2025-09-04

  • From This Site

    /content/journals/cucs/10.2174/0129503779378012250613194901
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. SinghP.A. BajwaN. SharmaL. BaldiA. Farm-level medicinal plants production and economic analysis in the Indian state of Punjab.J. Appl. Res. Med. Aromat. Plants202334110048610.1016/j.jarmap.2023.100486
     [Google Scholar]
  2. BaldiA. Regulatory and quality concerns for healthcare products.Appl. Clin. Res. Clin. Trials Regul. Aff.2017412310.2174/2213476X0401170227203629
     [Google Scholar]
  3. EkorM. The growing use of herbal medicines: Issues relating to adverse reactions and challenges in monitoring safety.Front. Pharmacol.2014417710.3389/fphar.2013.0017724454289
     [Google Scholar]
  4. EverstineK. SpinkJ. KennedyS. Economically motivated adulteration (EMA) of food: Common characteristics of EMA incidents.J. Food Prot.201376472373510.4315/0362‑028X.JFP‑12‑39923575142
     [Google Scholar]
  5. DubeyK. DubeyK. Biodiversity conservation of medicinal plants.J. Res. Educ. Indian Med.2011171-216
     [Google Scholar]
  6. SharmaV. SarkarI.N. Bioinformatics opportunities for identification and study of medicinal plants.Brief. Bioinform.201314223825010.1093/bib/bbs02122589384
     [Google Scholar]
  7. LuoD. FanD. YuH. LiZ. A new processing technique for the identification of Chinese herbal medicine2013International Conference on Computational and Information Sciences ShiyangChina21-23 June 201347447710.1109/ICCIS.2013.131
     [Google Scholar]
  8. LiuC. WuX. XiongW. Chinese herbal medicine classification based on BP neural network.J. Softw.20149493894410.4304/jsw.9.4.938‑943
     [Google Scholar]
  9. KambojA. KaurI. KaurN. Recent advances in standardization of herbal drugs.Curr. Tradit. Med.20206427829910.2174/2215083805666190613141613
     [Google Scholar]
  10. TechenN. CrockettS. KhanI. SchefflerB. Authentication of medicinal plants using molecular biology techniques to compliment conventional methods.Curr. Med. Chem.200411111391140110.2174/092986704336520615180573
     [Google Scholar]
  11. Gurib-FakimA. Medicinal plants: Traditions of yesterday and drugs of tomorrow.Mol. Aspects Med.200627119310.1016/j.mam.2005.07.00816105678
     [Google Scholar]
  12. SahooN. ManchikantiP. DeyS. Herbal drugs: Standards and regulation.Fitoterapia201081646247110.1016/j.fitote.2010.02.00120156530
     [Google Scholar]
  13. ZhangY. WuL. Classification of fruits using computer vision and a multiclass support vector machine.Sensors2012129124891250510.3390/s12091248923112727
     [Google Scholar]
  14. JolyA. GoëauH. BonnetP. BakićV. BarbeJ. SelmiS. YahiaouiI. CarréJ. MouyssetE. MolinoJ-F. BoujemaaN. BarthélémyD. Interactive plant identification based on social image data.Ecol. Inform.201423223410.1016/j.ecoinf.2013.07.006
     [Google Scholar]
  15. WäldchenJ. MäderP. Plant species identification using computer vision techniques: A systematic literature review.Arch. Comput. Methods Eng.201825250754310.1007/s11831‑016‑9206‑z29962832
     [Google Scholar]
  16. HeublG. New aspects of DNA-based authentication of Chinese medicinal plants by molecular biological techniques.Planta Med.201076171963197410.1055/s‑0030‑125051921058240
     [Google Scholar]
  17. KorirN.K. HanJ. ShangguanL. WangC. KayeshE. ZhangY. FangJ. Plant variety and cultivar identification: Advances and prospects.Crit. Rev. Biotechnol.201333211112510.3109/07388551.2012.67531422698516
     [Google Scholar]
  18. ChenS. PangX. SongJ. ShiL. YaoH. HanJ. LeonC. A renaissance in herbal medicine identification: From morphology to DNA.Biotechnol. Adv.20143271237124410.1016/j.biotechadv.2014.07.00425087935
     [Google Scholar]
  19. ZhangY.B. ButP.P.H. WangZ.T. ShawP.C. Current approaches for the authentication of medicinal Dendrobium species and its products.Plant Genet. Resour.20053214414810.1079/PGR200578
     [Google Scholar]
  20. SucherN. CarlesM. Genome-based approaches to the authentication of medicinal plants.Planta Med.200874660362310.1055/s‑2008‑107451718449847
     [Google Scholar]
  21. ChandaS. Importance of pharmacognostic study of medicinal plants: An overview.J. Pharmacogn. Phytochem.2014256973
     [Google Scholar]
  22. KaurK. Artificial intelligence (AI) as a transitional tool for sustainable food systemsIn:Springer Nature SwitzerlandCham2023305328
     [Google Scholar]
  23. The power of AI in Ayurveda: Enhancing herb identification for safe and effective productsAvailable from: https://www.expresscomputer.in/guest-blogs/the-power-of-ai-in-ayurveda-enhancing-herb-identification-for-safe-and-effective-products/101615/#:~:text=Using%20techniques%20such%20as%20nea r,formulations%20and%20preventing%20spurious%20products
  24. YikM.H.Y. WongV.C.W.T. WongT.H. ShawP.C. HerBChain, a blockchain-based informative platform for quality assurance and quality control of herbal products.J. Tradit. Complement. Med.202111659860010.1016/j.jtcme.2021.07.00534765524
     [Google Scholar]
  25. WangH. ChenY. WangL. LiuQ. YangS. WangC. Advancing herbal medicine: Enhancing product quality and safety through robust quality control practices.Front. Pharmacol.202314126517810.3389/fphar.2023.126517837818188
     [Google Scholar]
  26. GastonK.J. O’NeillM.A. Automated species identification: Why not?Philos. Trans. R. Soc. Lond. B Biol. Sci.2004359144465566710.1098/rstb.2003.144215253351
     [Google Scholar]
  27. JoshiK. ChavanP. WarudeD. PatwardhanB. Molecular markers in herbal drug technology.Curr. Sci.2004872159165
     [Google Scholar]
  28. ZhaoZ. HuY. LiangZ. YuenJ. JiangZ. LeungK. Authentication is fundamental for standardization of Chinese medicines.Planta Med.2006721086587410.1055/s‑2006‑94720916902852
     [Google Scholar]
  29. LiT. ZhangH. Application of microscopy in authentication of traditional Tibetan medicinal plants of five Rhodiola (Crassulaceae) alpine species by comparative anatomy and micromorphology.Microsc. Res. Tech.200871644845810.1002/jemt.2057018300292
     [Google Scholar]
  30. SinghD. AeriV. AnanthanarayanaD.B. Development of standard operating protocol for slide preparation of powdered bark samples with varying grinding techniques.Pharmacogn. J.201810226527110.5530/pj.2018.2.47
     [Google Scholar]
  31. SultanaS. KhanM.A. AhmadM. BanoA. ZafarM. ShinwariZ.K. Authentication of herbal medicine neem (Azadirachta indica A. Juss.) by using taxonomic and pharmacognostic techniques.Pak. J. Bot.201143141150
     [Google Scholar]
  32. YadavR.N.S. AgarwalaM. Phytochemical analysis of some medicinal plants.J. Phytol.20113121014
     [Google Scholar]
  33. MirM.A. SawhneyS.S. JassalM.M.S. Qualitative and quantitative analysis of phytochemicals of Taraxacum officinale.Wudpecker J. Pharm. Pharmacol.20132115
     [Google Scholar]
  34. BullL. LanziP.L. StolzmannW. Learning classifier systems.Soft Comput.200263-414314310.1007/s005000100110
     [Google Scholar]
  35. SharmaS. NamanS. DwivediJ. BaldiA. Artificial intelligence-based smart identification system using herbal imagesIn: Applications of Optimization and Machine Learning in Image Processing and IoT. Chapman and Hall/CRC eBooks,2024123155
     [Google Scholar]
  36. ShabrinaN.H. LikaR.A. IndartiS. Deep learning models for automatic identification of plant-parasitic nematode.Artificial Intelligence in Agriculture2023711210.1016/j.aiia.2022.12.002
     [Google Scholar]
  37. ChangE. ChangS.F. HauptmannA.G. HuangT.S. SlaneyM. Web-scale multimedia processing and applications.Proc. IEEE2012100925802583[Scanning the issue]. [http://dx.doi.org/10.1109/JPROC.2012.2204110
     [Google Scholar]
  38. PushpanathanK. Machine learning in medicinal plants recognition: A review.Artif. Intell. Rev.2021541305327
     [Google Scholar]
  39. SinghP.A. BajwaN. NamanS. BaldiA. A review on robust computational approaches based identification and authentication of herbal raw drugs.Lett. Drug Des. Discov.20201791066108310.2174/1570180817666200304125520
     [Google Scholar]
  40. SonJ. ShinJ.Y. KimH.D. JungK.H. ParkK.H. ParkS.J. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images.Ophthalmology20201271859410.1016/j.ophtha.2019.05.02931281057
     [Google Scholar]
  41. NamanS. SharmaS. KumarM. KumarM. BaldiA. Developing a CNN-based machine learning model for cardamom identification: A transfer learning approach.Lat. Am. J. Pharm.2023426565574[a
     [Google Scholar]
  42. NamanS. SharmaS. BaldiA. Machine learning based identification of spices: A case study of chilli.Lat Am J. Pharm. Life. Sci. J.20234210248261[b
     [Google Scholar]
  43. SunX. QianH. Chinese herbal medicine image recognition and retrieval by convolutional neural network.PLoS One2016116e015632710.1371/journal.pone.015632727258404
     [Google Scholar]
  44. RajiI.K. ThyagharajanK.K. An analysis of segmentation techniques to identify herbal leaves from complex background.Procedia Comput. Sci.20154858959910.1016/j.procs.2015.04.140
     [Google Scholar]
  45. PujariJ.D. YakkundimathR. ByadgiA.S. Image processing based detection of fungal diseases in plants.Procedia Comput. Sci.2015461802180810.1016/j.procs.2015.02.137
     [Google Scholar]
  46. AnamiB.S. NandyalS.S. GovardhanA. A combined color, texture and edge features based approach for identification and classification of Indian medicinal plants.Int. J. Comput. Appl.2010612455110.5120/1122‑1471
     [Google Scholar]
  47. MiaoJ. HuangY. WangZ. WuZ. LvJ. Image recognition of traditional Chinese medicine based on deep learning.Front. Bioeng. Biotechnol.202311119980310.3389/fbioe.2023.119980337545883
     [Google Scholar]
  48. ShailendraR. JayapalanA. VelayuthamS. BaladhandapaniA. SrivastavaA. Kumar GuptaS. KumarM. An IoT and machine learning based intelligent system for the classification of therapeutic plants.Neural Process. Lett.20225454465449310.1007/s11063‑022‑10818‑5
     [Google Scholar]
  49. ConcepcionR.S. BedruzR.A.R. CulabaA.B. DadiosE.P. PascuaA.R.A.R. The technology adoption and governance of artificial intelligence in the Philippines201910.1109/HNICEM48295.2019.9072725
     [Google Scholar]
  50. NamanS. SharmaS. BaldiA. Identification of spices and their adulterants by integrating machine learning and analytical techniques: A representative study.Int. J. Pharm. Sci. Drug Res.2024163359369[a10.25004/IJPSDR.2024.160307
     [Google Scholar]
  51. De LunaR.G. BaldovinoR.G. CotocoE.A. De OcampoA.L.P. ValenzuelaI.C. CulabaA.B. GokongweiE.P.D. Identification of Philippine herbal medicine plant leaf using artificial neural network2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)2017 Manila, Philippines3 December 201718201710.1109/HNICEM.2017.8269470
     [Google Scholar]
  52. ZhangS. ZhangS. ZhangC. WangX. ShiY. Cucumber leaf disease identification with global pooling dilated convolutional neural network.Comput. Electron. Agric.201916242243010.1016/j.compag.2019.03.012
     [Google Scholar]
  53. WuC. ChenJ. Lai-Han LeungE. ChangH. WangX. Editorial: Artificial intelligence in traditional medicine.Front. Pharmacol.20221393313310.3389/fphar.2022.93313335991902
     [Google Scholar]
  54. SharmaS. NamanS. DwivediJ. BaldiA. Artificial intelligence-based smart identification system using herbal images: Decision making using various machine learning models.Applications of Optimization and machine learning in Image Processing and IoT.Chapman and Hall/CRC202312315510.1201/9781003364856‑9
     [Google Scholar]
  55. NamanS. SharmaS. BaldiA. A CNN-Based Machine Learning Model for the identification of herbal drugs: A case study on cumin.Current Computer Science202410.2174/0129503779301717240607070206
     [Google Scholar]
  56. KumarN. BelhumeurP.N. BiswasA. JacobsD.W. KressW.J. LopezI.C. SoaresJ.V. Leafsnap: A computer vision system for automatic plant species identificationComputer Vision – ECCV 2012, Lecture Notes in Computer Science7573SpringerBerlin, Heidelberg2012502516
     [Google Scholar]
  57. GoëauH. BonnetP. JolyA. AffouardA. BakicV. BarbeJ. BoujemaaN. Pl@ntNet mobile 2014: Android port and new featuresProceedings of International Conference on Multimedia RetrievalGlasgow, United Kingdom1 April 2014527528201410.1145/2578726.2582618
     [Google Scholar]
  58. MunisamiT. RamsurnM. KishnahS. PudaruthS. Plant leaf recognition using shape features and colour histogram with K-nearest neighbour classifiers.Procedia Comput. Sci.20155874074710.1016/j.procs.2015.08.095
     [Google Scholar]
  59. BarréP. StöverB.C. MüllerK.F. SteinhageV. LeafNet: A computer vision system for automatic plant species identification.Ecol. Inform.201740505610.1016/j.ecoinf.2017.05.005
     [Google Scholar]
  60. ZhaoZ.Q. MaL.H. CheungY. WuX. TangY. ChenC.L.P. ApLeaf: An efficient android-based plant leaf identification system.Neurocomputing20151511112111910.1016/j.neucom.2014.02.077
     [Google Scholar]
  61. Mehdipour GhaziM. YanikogluB. AptoulaE. Plant identification using deep neural networks via optimization of transfer learning parameters.Neurocomputing201723522823510.1016/j.neucom.2017.01.018
     [Google Scholar]
  62. LaxmiG.F. HerdiyeniY. ArkemanY. Identification of medicinal plant by fuzzy local binary pattern and multi-objective genetic algorithm2017 International Conference on Computer, Control, Informatics and its Applications (IC3INA)Jakarta, Indonesia23–26 October 20172934201710.1109/IC3INA.2017.8251735
     [Google Scholar]
  63. CulmanM.A. GomezJ.A. TalaveraJ. QuirozL.A. TobonL.E. ArandaJ.M. BayonaC.J. A novel application for identification of nutrient deficiencies in oil palm using the internet of things5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) 169172201710.1109/MobileCloud.2017.32
     [Google Scholar]
  64. ElhassounyA. SmarandacheF. Smart mobile application to recognize tomato leaf diseases using convolutional neural networks2019 International Conference of Computer Science and Renewable Energies (ICCSRE)Agadir, Morocco24 July 201914201910.1109/ICCSRE.2019.8807737
     [Google Scholar]
  65. AkiyamaT. KobayashiY. SasakiY. SasakiK. KawaguchiT. KishigamiJ. Mobile leaf identification system using CNN applied to plants in Hokkaido2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)Osaka, Japan15–18 October 2019324325201910.1109/GCCE46687.2019.9015298
     [Google Scholar]
  66. TangY. ChenM. WangC. LuoL. LiJ. LianG. ZouX. Recognition and localization methods for vision-based fruit picking robots: A review.Front Plant. Sci.20201151010.3389/fpls.2020.0051032508853
     [Google Scholar]
  67. FerentinosK.P. Deep learning models for plant disease detection and diagnosis.Comput. Electron. Agric.201814531131810.1016/j.compag.2018.01.009
     [Google Scholar]
  68. HilalPJ HarishP. HarshitaC. AshishkumarP. RashmiD. SanskrutiS. Advancements and challenges in the integration of artificial intelligence with herbal medicine researchIJPS Journal2024
     [Google Scholar]
  69. DevS.A. UnnikrishnanR. JayarajR. SujanapalP. AnithaV. Quantification of adulteration in traded ayurvedic raw drugs employing machine learning approaches with DNA barcode database3 Biotech2021111146310.1007/s13205‑021‑03001‑5
     [Google Scholar]
  70. VeraM.C.S. PalaoagT.D. PalaoagT.D. Implementation of a smarter herbal medication delivery system employing an AI-powered chatbot.Int. J. Adv. Comput. Sci. Appl.202314310.14569/IJACSA.2023.0140358
     [Google Scholar]
  71. LoveC.S. “Just the facts, Ma’am”: Moral and ethical considerations for artificial intelligence in medicine and its potential to impact patient autonomy and hope.Linacre Q.202390437539410.1177/00243639231162431
     [Google Scholar]
  72. PatelJ. PatelD. MeshramD. Artificial intelligence in pharma industry-a rising concept.J. Adv. Pharmacogn2021125464
     [Google Scholar]
  73. HuntR. CaustonD.R. ShipleyB. AskewA.P. A modern tool for classical plant growth analysis.Ann. Bot.200290448548810.1093/aob/mcf21412324272
     [Google Scholar]
  74. TessmerO.L. JiaoY. CruzJ.A. KramerD.M. ChenJ. Functional approach to high-throughput plant growth analysisBMC Systems Biology20137S6S1710.1186/1752‑0509‑7‑S6‑S1724565437
     [Google Scholar]
  75. BaldiA. Computational approaches for drug design and discovery: An overview.Syst. Rev. Pharm.2010119910.4103/0975‑8453.59519
     [Google Scholar]
/content/journals/cucs/10.2174/0129503779378012250613194901
Loading
Artificial Intelligence in Herbal Drug Authentication: Revolutionizing Identification, Adulterant Detection and Standardization
Curr. Comp. Sci. 4, E29503779378012 (2025); https://doi.org/10.2174/0129503779378012250613194901
/content/journals/cucs/10.2174/0129503779378012250613194901
/content/journals/cucs/10.2174/0129503779378012250613194901
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): Adulteration; artificial neural network; convolutional neural network; herbal drugs; machine learning; standardization
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test