Authors : Sanduni Jayamali Gamage K.G.; Athapaththu P.N.P.; Nandu Gamitha Manawadu; Hansi De Silva

Volume/Issue : Volume 9 - 2024, Issue 5 - May

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DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1928

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Abstract : Sri Lanka is a country with a Ayurvedic culture which cannot be experienced anywhere in the world. This cultural system is based on a series of knowledge passed on from generations over 3000 years that could treat a variety of diseases. This traditional ayurvedic system consist of a vast herbal plant collection. Most of the information about this ayurvedic system is written in manuscripts for thousands of years. Sri Lanka lacks a proper system which is specific to ayurvedic sector is a major concern that should be addressed at present. Absence of a system has lead to problems and difficulties in identification and classification of herbal plants, to transfer knowledge about herbal plants and to conserve these ayurvedic plants for the future generation. Another concern is that Ayurvedic undergraduate students face many difficulties when gathering knowledge of these herbal plants and medicinal practices. Sri Lanka does not comprise with a full ayurvedic plant inventory system is another major concern that identified in the country. By considering all the problems an intelligent system has been recognized as a solution. The system will be based on Deep Learning, CNN, GIS, Artificial Intelligence and Machine Learning based principals to cater all the identified problems. The system will be able to identify ayurvedic plant with an image of a leave, flower, or fruit as input. And also, system will be able to classify and provide a detailed description about the identified plant including medicinal value and the distribution of the plant in the island. System will provide a crowdsourcing social media facility with both English and Sinhala languages to share information with fellow herbalist in the country.

Keywords : Ayurveda, Deep Learning, CNN, Machine Learning, Artificial Intelligence, NLP, Crowdsourcing, AutoML, GIS.

References :

1. T. N. M. Kankanamalage, R. M. Dharmadasa, D. C. Abeysinghe, and R. G. S. Wijesekara, “A survey on medicinal materials used in traditional systems of medicine in Sri Lanka,” J. Ethnopharmacol., vol. 155, no. 1, pp. 679–691, 2014.
2. P. B. Weragoda, “The traditional system of medicine in Sri Lanka,” J. Ethnopharmacol., vol. 2, no. 1, pp. 71–73, 1980.
3. K. Priya, S. Ranjana, and R. Manimegala, “Medicinal Plant Identification using Android Application based on leaf image,” Eur. J. Mol. Clin. Med., vol. 7, no. 09, pp. 1496–1506, 2020, doi: 10.31838/ejmcm.07.09.161.
4. Plantnet-project.org. [Online]. Available: https://identify.plantnet-project.org/. [Accessed: 23-Jan-2022].
5. “PlantSnap - plant identifier app, #1 mobile app for plant identification,” Plantsnap - Identify Plants, Trees, Mushrooms With An App, 24-Aug-2018. [Online]. Available: https://www.plantsnap.com/. [Accessed: 23-Jan-2022].
6. “There’s an app for us ·,” iNaturalist. [Online]. Available: https://www.inaturalist.org/pages/mobile_apps_nz. [Accessed: 23-Jan-2022].
7. “The flora incognita app,” Floraincognita.com. [Online]. Available: https://floraincognita.com/flora-incognita-app/. [Accessed: 23-Jan-2022].
8. P.V.D Nadeeshan, A.D.A.D.S Jayalath, T.G.A.G.D Amarawansha and H.P Jayasuriya, “Deeghayu - Ayurveda Information Centralized Mobile Application” (2019).
9. M. R. Dileep and P. N. Pournami, “AyurLeaf: A Deep Learning Approach for Classification of Medicinal Plants,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2019-October, pp. 321–325, 2019, doi: 10.1109/TENCON.2019.8929394.
10. N. Pathiranage, N. Nilfa, M. Nithmali, N. Kumari, L. Weerasinghe, and I. Weerathunga, “Arogya -An Intelligent Ayurvedic Herb Management Platform,” in 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 2020.
11. K. Anuradha, D. P. M. Lakshan, and R. P. S. Kathriarachchi, “A Study on the Ayurveda Plant Recognition for Remedial Medications Using Image Processing Techniques,” pp. 515–522
12. M. Jayanka and T. G. I. Fernando, “Recognising Ayurvedic Herbal Plants in Sri Lanka using Convolutional Neural Networks,” Vidyodaya J. Sci., vol. 23, pp. 48–60, 2020
13. Dahiwade, G. Patle, and E. Meshram, “Designing disease prediction model using machine learning approach,” Proc. 3rd Int. Conf. Comput. Methodol. Commun. ICCMC 2019, no. Iccmc, pp. 1211–1215, 2019, doi: 10.1109/ICCMC.2019.8819782
14. R. I. S. Bandara, S. Prabagaran, S. A. K. G. Perera, M. N. R. Banu, and K. A. D. C. P. Kahandawaarachchi, “Wedaduru -An Intelligent Ayurvedic Disease Screening and Remedy Analysis Solution,” 2019 Int. Conf. Adv. Comput. ICAC 2019, pp. 151–155, 2019, doi: 10.1109/ICAC49085.2019.9103410
15. Nagarajah and G. Poravi, “An extensive checklist for building autoML systems,” CEUR Workshop Proc., vol. 2360, no. April,2019.
16. J. Gupta, V. Singh and I. Kumar, "Florence- A Health Care Chatbot," 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp. 504-508, doi:K
17. S. Hussain and G. Athula, "Extending a Conventional Chatbot Knowledge Base to External Knowledge Source and Introducing User Based Sessions for Diabetes Education," 2018 32nd International Conference on
18. Rule-based Chabot for student enquiries to cite this article: Jagdish Singh et al 2019 J. Phys.: Conf. Ser. 1228 012060.
19. D. A. D. S. Jayalath, P. V. D. Nadeeshan, T. G. A. G. D. Amarawansh, H. P. Jayasuriya and D. P. Nawinna, "Ayurvedic Knowledge Sharing Platform with Sinhala Virtual Assistant," 2019 International Conference on Advancements in Computing (ICAC), 2019, pp. 220-225, doi: 10.1109/ICAC49085.2019.9103413.
20. K. Shanmugalingam and S. Sumathipala, "Language identification at word level in Sinhala-English code-mixed social media text," 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), 2019, pp. 113-118, doi: 10.23919/SCSE.2019.8842795.
21. D. Zhao, N. Du, Z. Chang and Y. Li, "Keyword Extraction for Social Media Short Text," 2017 14th Web Information Systems and Applications Conference (WISA), 2017, pp. 251-256, doi: 10.1109/WISA.2017.12.
22. Wikipedia contributors. (2021, December 3). Artificial Linguistic Internet Computer Entity. Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Artificial_Linguistic_Internet_Computer_Entity&oldid=1058370204
23. S. Meshram, N. Naik, M. VR, T. More and S. Kharche, "College Enquiry Chatbot using Rasa Framework," 2021 Asian Conference on Innovation in Technology (ASIANCON), 2021, pp. 1-8, doi: 10.1109/ASIANCON51346.2021.9544650.
24. Anandhan, L. Shuib, M. A. Ismail, and G. Mujtaba, “Social Media Recommender Systems: Review and Open Research Issues,” IEEE Access, vol. 6, no. Figure 1, pp. 15608–15628, 2018, doi: 10.1109/ACCESS.2018.2810062.
25. Zhang X, Shang L, Yuan Y (2017) A crowd wisdom management framework for crowdsourcing systems. IEEE Access 4:9764–9774
26. Xing Y, Wang L, Li Z (2019) Multi-Attribute crowdsourcing task assignment with stability and satisfactory. IEEE Access 7:133351–133361
27. Tran L, To H, Fan L (2018) A real-time framework for task assignment in hyperlocal spatial crowdsourcing. ACM Trans Intell Syst Technol 9(3):37:1–37:26
28. Alireza S, Shafigheh H, Masoud RA (2018) Personality classification based on profiles of social networks’ users and the five-factor model of personality. Hum-Cent Comput Inform Sci 8(1):24–38
29. Narducci et al., (2013, Aug). Exploiting big data for enhanced representations in content-based recommender systems. Presented at International Conference on EC Web 2013. [Online] Available :
30. Z. Yang, B.Wu, K. Zheng, X.Wang, and L. Lei, ``A survey of collaborative filtering-based recommender systems for mobile Internet applications,'' IEEE Access, vol. 4, pp. 3273_3287, 2016.
31. B. Huang, X. Yan, and J. Lin, ``Collaborative filtering recommendation algorithm based on joint nonnegative matrix factorization,'' Pattern Recognit. Artif. Intell., vol. 29, no. 6, pp. 725_734, Aug. 2016.
32. Wang, T. Li, X. Wang, S. Zhu, and C. Ding, ``Community discovery using nonnegative matrix factorization,'' Data Mining Knowl. Discovery, vol. 22, no. 3, pp. 493_521, 2011.
33. J. Cheng, Y. Liu, H. Zhang, X. Wu, and F. Chen, “A new recommendation algorithm based on user’s dynamic information in complex social network,” Math. Probl. Eng., vol. 2015, 2015, doi: 10.1155/2015/281629.
34. Takizawa et al., “Plant recognition by integrating color and range data obtained through stereo vision,” J. adv. comput. intell. intell. inform., vol. 9, no. 6, pp. 630–636, 2005.
35. D. Andújar, M. Calle, C. Fernández-Quintanilla, Á. Ribeiro, and J. Dorado, “Three-dimensional modeling of weed plants using low-cost photogrammetry,” Sensors (Basel), vol. 18, no. 4, p. 1077, 2018.
36. E. Chong and H. H. Harith, “Performance of structure-from-motion approach on plant phenotyping using images from smartphone,” IOP Conf. Ser. Earth Environ. Sci., vol. 1038, no. 1, p. 012031, 2022.
37. T. Nagarajah and G. Poravi, “A review on automated machine learning (AutoML) systems,” in 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019
38. U. Shardanand, and P. Maes, (1995, May) Social information filtering: Algorithms for automating ‘word of mouth. Presented at CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. [Online]