Abstract
Proper identification of medicinal plants is essential for agronomists, ayurvedic medicinal practitioners and for ayurvedic medicines industry. Even though many plant leaf databases are available publicly, no specific standardized database is available for Indian Ayurvedic Plant species. In this paper, we introduce a publicly available annotated database of Indian medicinal plant leaf images named as MepcoTropicLeaf. The research work also presents the preliminary results on recognizing the plant species based on the spatial, spectral and machine learnt features on the selected set of 50 species from the database. To attain the machine learnt features, we propose a six level convolutional neural network (CNN) and report an accuracy of 87.25% using machine learnt features.
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References
Ayurvedic Medicine, University of Minnesota’s Center for Spirituality & Healing. https://www.takingcharge.csh.umn.edu/explore-healing-practices/ayurvedic-medicine
Chen, Y.F., Liu, H., Chen, Q.: Status and prospect for the study of plants identification methods. World Forest. Res. 27(4), 18–22 (2014)
Dileep, M.R., Pournami, P.N.: AyurLeaf: a deep learning approach for classification of medicinal plants. In: TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 2019, pp. 321–325. https://doi.org/10.1109/TENCON.2019.8929394
Chaki, J., Parekh, R., Bhattacharya, S.: Recognition of whole and deformed plant leaves using statistical shape features and neuro-fuzzy classifier. In: 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), pp. 189–194 (2015b). https://doi.org/10.1109/ReTIS.2015.7232876
Hossain, J., Amin, M.: Leaf shape identification based plant biometrics. In: 2010 13th International Conference on Computer and Information Technology (ICCIT), pp. 458–463 (2010). https://doi.org/10.1109/ICCITECHN.2010.5723901
Watcharabutsarakham, S., Sinthupinyo, W., Kiratiratanapruk, K.: Leaf classification using structure features and support vector machines. In: 2012 6th International Conference on New Trends in Information Science and Service Science and Data Mining (ISSDM), pp. 697–700 (2012)
Nesaratnam, R.J., Bala Murugan, C.: Identifying leaf in a natural image using morphological characters. In: 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–5 (2015). https://doi.org/10.1109/ICIIECS.2015.7193115
Du, J.X., Wang, X.F., Zhang, G.J.: Leaf shape based plant species recognition. Appl. Math. Comput. 185, 883–893 (2007)
Chathura Priyankara, H., Withanage, D.: Computer assisted plant identification system for Android. In: 2015 Moratuwa Engineering Research Conference (MERCon), pp. 148–153 (2015). https://doi.org/10.1109/MERCon.2015.7112336
Hsiao, J.K., Kang, L.W., Chang, C.L., Lin, C.Y.: Comparative study of leaf image recognition with a novel learning-based approach. In: 2014 Science and Information Conference (SAI), pp. 389–393 (2014). https://doi.org/10.1109/SAI.2014.6918216
Lavania, S., Matey, P.S.: Leaf recognition using contour based edge detection and sift algorithm. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4 (2014). https://doi.org/10.1109/ICCIC.2014.7238345
Islam, M.A., Yousuf, M.S.I., Billah, M.M.: Automatic plant detection using HOG and LBP features with SVM. Int. J. Comput. (IJC) 33(1), 26–38 (2019)
Pham, N.H., Le, T.L., Grard, P., Nguyen, V.N.: Computer aided plant identification system. In: 2013 International Conference on Computing, Management and Telecommunications (ComManTel), pp. 134–139 (2013). https://doi.org/10.1109/ComManTel.2013.6482379
Kherkhah, F.M., Asghari, H.: Plant leaf classification using GIST texture features. IET Comput. Vis. 13, 36 (2019)
Kumar, P.M., Surya, C.M., Gopi, V.P.: Identification of ayurvedic medicinal plants by image processing of leaf samples. In: 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, 2017, pp. 231–238. https://doi.org/10.1109/ICRCICN.2017.8234512
Ahila Priyadharshini, R., Arivazhagan, S., Arun, M., Mirnalini, A.: Maize leaf disease classification using deep convolutional neural networks. Neural Comput. Appl. 31(12), 8887–8895 (2019). https://doi.org/10.1007/s00521-019-04228-3
Vilasini, M., Ramamoorthy, P.: CNN approaches for classification of Indian leaf species using smartphones. CMC-Comput. Mater. Continua. 62(3), 1445–1472 (2020)
Dalal,N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, 2005, vol. 1, pp. 886–893. https://doi.org/10.1109/CVPR.2005.177
Acknowledgement
This research is done as a part of Kaggle’s Open Data Research Grant. We would also like to thank Dr. Jeyakumar for his help in verifying the plant species.
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Ahila Priyadharshini, R., Arivazhagan, S., Arun, M. (2021). Ayurvedic Medicinal Plants Identification: A Comparative Study on Feature Extraction Methods. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_23
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