SpringerLink
Log in

AyushNet – an IoT-based Mobile App for the Automatic Recognition of Medicinal Plants based on a deep residual neural network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Traditional medicinal herbs are increasingly being integrated into modern medicine by the pharmaceutical and healthcare industries due to their lower cost and fewer side effects. The classification of medicinal plants significantly impacts Traditional Indian Medicine (TIM) resource protection, authentication, and identification instruction. The global healthcare community has recently shown more interest in Indian medicinal plants and their therapeutic benefits. Consequently, most research focuses on automatically identifying medicinal plants using their leaf, flower, or other plant organs. This requires using DL techniques for robust recognition, as there is minimal variation in image texture and color. In this study, we introduce AyushNets, a self-supervised mobile application that eliminates manual classification and predicts the label of medicinal plants based on leaf images. The leaf images of medicinal plants are collected from mobile devices, and the preprocessed data is trained using the Inception-Resnet V2 architecture. The knowledge gained from the trained data is further enhanced through the Transfer Learning Modal with newly available data. AyushNet achieves an average success rate of 97% when analyzing leaf photos of 30 different medicinal plants. This outcome suggests that automatic classification of medicinal plants using leaf images is feasible. The study provides a valuable conceptual framework for investigating and develo** a classification system for medicinal plants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

This work is based on the Mendeley medicinal plant dataset. It is publicly available at https://data.mendeley.com/datasets/nnytj2v3n5/1.

References

  1. Adhikari B, Marasini BP, Rayamajhee B, Bhattarai BR, Lamichhane G, Khadayat K, Parajuli N (2021) Potential roles of medicinal plants for treating viral diseases focusing on COVID-19: A review. Phytother Res 35(3):1298–1312

    Article  Google Scholar 

  2. Almazaydeh l, Alsalameen R, & Elleithy K. (2022). Herbal leaf recognition using mask-region convolutional neural network (mask r-CNN). J Theor Appl Inf Technol. 100(11).

  3. Altemimi A, Lakhssassi N, Baharlouei A, Watson DG, Lightfoot DA (2017) Phytochemicals: Extraction, isolation, and identification of bioactive compounds from plant extracts. Plants 6(4):42

    Article  Google Scholar 

  4. Amuthalingeswaran C, Sivakumar M, Renuga P, Alexpandi S, Elamathi J, Hari SS (2019) Identification of medicinal plants and their usage by using deep learning. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, pp 886–890

    Google Scholar 

  5. Blesslin Elizabeth CP, Baulkani S (2023) Novel network for medicinal leaves identification. IETE J Res 69(4):1772–1782

    Article  Google Scholar 

  6. Bhuiyan M, Abdullahil-Oaphy M, Khanam RS, Islam M (2021) MediNET: A deep learning approach to recognize Bangladeshi ordinary medicinal plants using CNN. In Soft Computing Techniques and Applications. Springer, Singapore, pp 371–380

    Google Scholar 

  7. Chakraborty P (2018) Herbal genomics as tools for dissecting new metabolic pathways of unexplored medicinal plants and drug discovery. Biochimie open 6:9–16

    Article  Google Scholar 

  8. Dileep MR, Pournami PN (2019) Ayurveda: a deep learning approach for classification of medicinal plants. In TENCON 2019–2019 IEEE Region 10 Conference (TENCON). IEEE, pp 321–325

    Chapter  Google Scholar 

  9. Ghosh S, Singh A, & Kumar S. (2024). HPB3C-3PG algorithm: A new hybrid global optimization algorithm and its application to plant classification. Ecol Inform. 102581.

  10. Gour N, Khanna P (2021) Multi-class multi-label ophthalmological disease detection using transfer learning-based convolutional neural network. Biomed Signal Process Control 66:102329

    Article  Google Scholar 

  11. Jan M, Mir TA, Jan HA, & Khare RK. (2022). Medicinal plants diversity and their uses for Gynecological Disorders of District Baramulla, Jammu and Kashmir, India. Vegetos, 1–15.

  12. Javid A, Haghirosadat BF (2017) A Review of Medicinal Plants Effective in the Treatment or Apoptosis of Cancer Cells. Cancer Press Journal 3(1):22–26

    Article  Google Scholar 

  13. Kaur PP, Singh S, Sandhu IS, & Sandhanwalia DS. (2022, February). Leaf Recognition System Based on Supervised Machine Learning Techniques. In 2022 IEEE Delhi Section Conference (DEFCON) (pp. 1–6). IEEE.

  14. Kumar S. (2021). MCFT-CNN: Malware classification with fine-tuned convolution neural networks using traditional and transfer learning in the Internet of Things. Future Generation Computer Systems, pp. 125, 334–351.

  15. Kumar H, Mamoria, P Dewangan DK, & Kumari S. (2024, February). Internet of Things (IoT): Role of Machine Learning in Power Management and Optimization using Regression Analysis. In 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) (Vol. 5, pp. 1319–1326). IEEE.

  16. Lee SH, Chan CS, Remagnino P (2018) Multiorgan plant classification based on convolutional and recurrent neural networks. IEEE Trans Image Process 27(9):4287–4301

    Article  MathSciNet  Google Scholar 

  17. Liu S, Chen W, & Dong X. (2018, July). Automatic classification of Chinese herbal based on deep learning method. In 2018, the 14th International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery (ICNC-FSKD) (pp. 235–238). IEEE.

  18. Liu Y, Li Y, Zhao Y, & Na X. (2021, October). Image Classification and Recognition of Medicinal Plants Based on Convolutional Neural Network. In 2021 IEEE 21st International Conference on Communication Technology (ICCT) (pp. 1128–1133). IEEE.

  19. Mukherjee G, Tudu B, Chatterjee A (2021) A convolutional neural network-driven computer vision system for identifying species and maturity stage of medicinal leaves: case studies with Neem, Tulsi, and Kalmegh leaves. Soft Comput 25(22):14119–14138

    Article  Google Scholar 

  20. Muneer A, Fati SM (2020) Efficient and automated herbs classification approach based on shape and texture features using deep learning. IEEE Access 8:196747–196764

    Article  Google Scholar 

  21. Omara T (2021) East African quintessential plants claimed to be used as blood purifiers, cleansers, detoxifiers and tonics: an appraisal of ethnobotanical reports and correlation with reported bioactivities. Bulletin of the National Research Centre 45(1):1–23

    Article  Google Scholar 

  22. Panmei R, Gajurel PR, Singh B (2019) Ethnobotany of medicinal plants used by the Zeliangrong ethnic group of Manipur, northeast India. J Ethnopharmacol 235:164–182

    Article  Google Scholar 

  23. Peng C, Liu Y, Yuan X, & Chen Q. (2022). Research of image recognition method based on enhanced inception-ResNet-V2. Multimedia Tools and Applications, 1–21.

  24. Putri YA, Djamal EC, & Ilyas R. (2021, March). Identification of medicinal plant leaves using convolutional neural network. In Journal of Physics: Conference Series (Vol. 1845, No. 1, p. 012026). IOP Publishing.

  25. Quoc TN, & Hoang VT. (2020, October). Medicinal Plant identification in the wild by using CNN. 2020 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 25–29). IEEE.

  26. Remya S, Sasikala R (2020) Performance evaluation of optimized and adaptive neuro-fuzzy inference system for predictive modeling in agriculture. Comput Electr Eng 86:106718

    Article  Google Scholar 

  27. Retiu A, Budescu T, Menae I (2021) Aloe Vera Extract For Stomach Acid Use Safe And Effective Treatment. Int J Papier Adv Sci Rev 2(2):59–64

    Google Scholar 

  28. Sarkar S, Vinay S, Djeddi C, & Maiti J. (2022). Classification and pattern extraction of incidents: a deep learning-based approach. Neural Computing and Applications, 1–22.

  29. Sayed AN, Himeur Y, Bensaali F (2022) Deep and transfer learning for building occupancy detection: a review and comparative analysis. Eng Appl Artif Intell 115:105254

    Article  Google Scholar 

  30. Shahrajabian MH, Sun W, Cheng Q (2022) The importance of flavonoids and phytochemicals of medicinal plants with antiviral activities. Mini-Rev Org Chem 19(3):293–318

    Article  Google Scholar 

  31. Sharma CM, Goyal L, Chariar VM, & Sharma N. (2022). Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing. J Healthcare Eng. 2022.

  32. Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inform Proc Agri 4(1):41–49

    Google Scholar 

  33. Sladojevic S, Arsenovic M, Anderla A, Culibrk D, & Stefanovic D. (2016). Deep neural networks-based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience, 2016.

  34. Szegedy C, Ioffe S, Vanhoucke V,  & Alemi AA. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence.

  35. Tiwari V, Joshi RC, & Dutta MK. (2022). Deep neural network for multi‐class classification of medicinal plant leaves. Expert Systems, e13041.

  36. Wäldchen J, Rzanny M, Seeland M, Mäder P (2018) Automated plant species identification—Trends and future directions. PLoS Comput Biol 14(4):e1005993

    Article  Google Scholar 

  37. World Health Organization. (1999). WHO monographs selected medicinal plants (Vol. 2). World Health Organization.

  38. Yu X, Wang J, Hong QQ, Teku R, Wang SH, Zhang YD (2022) Transfer learning for medical images analyses: A survey. Neurocomputing 489:230–254

    Article  Google Scholar 

  39. Zhu W, Braun B, Chiang LH, Romagnoli JA (2021) Investigation of transfer learning for image classification and impact on training sample size. Chemom Intell Lab Syst 211:104269

    Article  Google Scholar 

Download references

Funding

We have not received any funding for this project.

Author information

Authors and Affiliations

  1. Department of CSE, School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India

    Sasikaladevi N & Revathi A

Authors
  1. Sasikaladevi N
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Revathi A
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sasikaladevi N.

Ethics declarations

Declarations

We declare we did the above work, and it is neither published nor considered for any other publications.

Ethical approval

The above work is not based on animal or real-time human data. It is based on the data from the public repository.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

N, S., A, R. AyushNet – an IoT-based Mobile App for the Automatic Recognition of Medicinal Plants based on a deep residual neural network. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19442-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-19442-y

Keywords

Search

Navigation