Abstract
Plants are an obligatory piece of our biological system, and the decreasing number of plant assortments is a genuine concern. To conserve plants and make optimum utilization of them, it is a major requirement to identify them based on their discrete essential features and properties. Plants structure the foundation of Ayurveda, and the present modern-day medication is an extraordinary wellspring of revenue. Leaf identification by mechanical means frequently prompts wrong recognizability. Here, we are mentioning the idea of mapping the morphological/physical features of leaves, plants, and herbs with the active biochemical compound in the equivalent. Despite the fact, physical features are not associated with the chemical compound in leaf/plants; we can use both types of features to gain a good outcome in the identification and classification of medicinal leaves/plants. Solely morphological features or bio-active compounds in the leaves are not adequate to acquire the precise results in the prediction model. In this paper, we have described the combined tabular data of plants and leaves that incorporate the morphological as well as chemical features of individual leaves/plants/herbs from around 20 countries and 4 continents in the world. Also, there is a clear description of methods that can be used for generating such a prediction model using machine learning techniques (considering the state of the artwork).
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References
Gopal, A., Prudhveeswar Reddy, S., Gayatri, V.: Classification of selected medicinal plants leaf using image processing. In: 2012 International Conference on Machine Vision and Image Processing, MVIP 2012, (December), 5–8 (2012). https://doi.org/10.1109/MVIP.2012.6428747
Pandey, M.M., Rastogi, S., Rawat, A.K.S.: Indian traditional ayurvedic system of medicine and nutritional supplementation. Evid.-Based Complement. Altern. Med. (2013)
Ching, J., Soh, W.L., Tan, C.H., Lee, J.F., Tan, J.Y.C., Yang, J., Yap, C.W., Koh, H.L.: Identification of active compounds from medicinal plant extracts using gas chromatography-mass spectrometry and multivariate data analysis. J. Sep. Sci. 35(1), 53–59 (2012)
Petrovska, B.B.: Historical review of medicinal plants’ usage. Pharmacognosy Rev. 6(11), 1–5 (2012)
Singh, R.: Medicinal plants: a review. J. Plant Sci. 3(1), 50 (2015)
Geck, M.S., Cabras, S., Casu, L., Reyes García, A.J., Leonti, M.: The taste of heat: how humoral qualities act as a cultural filter for chemosensory properties guiding herbal medicine. J. Ethnopharmacol. 198, 499–515 (2017)
Albuquerque, U.P., Ferreira Júnior, W.S.: What do we study in evolutionary ethnobiology? Defining the theoretical basis for a research program. Evol. Biol. 44, 206–215 (2017)
Albuquerque, U.P., Alves, R.R.N. (eds.): Introduction to Ethnobiology. Springer International Publishing, N.p. (2016). https://doi.org/10.1007/978-3-319-28155-1
Mustafa, G., Arif, R., Atta, A., Sharif, S., Jamil, A.: Bioactive compounds from medicinal plants and their importance in drug discovery in Pakistan. Matrix Sci. Pharma 1(1), 17–26 (2017)
Mukherjee, P.K., Wahile, A.: Integrated approaches towards drug development from Ayurveda and other Indian systems of medicines. J. Ethnopharmacol. 103, 25–35 (2006)
Mukherjee, P.K., Rai, S., Kumar, V., Mukherjee, K., Hylands, P.J., Hider, R.C.: Plants of Indian origin in drug discovery. Expert Opin. Drug Discov. 2(5), 633–657 (2007)
Xue, C.X., Zhang, X.Y., Liu, M.C., Hu, Z.D., Fan, B.T.: Study of probabilistic neural networks to classify the active compounds in medicinal plants. J. Pharm. Biomed. Anal. 38(3), 497–507 (2005)
Azlah, M.A.F., Chua, L.S., Rahmad, F.R., Abdullah, F.I., Alwi, S.R.W.: Review of techniques for plant leaf classification and recognition. Computers 8(4) (2019)
Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.X., Chang, Y.F., Xiang, Q.L.: A leaf recognition algorithm for plant classification using a probabilistic neural network. In:ISSPIT 2007—2007 IEEE International Symposium on Signal Processing and Information Technology, 11–16 (2007)
Shoemaker, M., Hamilton, B., Dairkee, S.H., Cohen, I., Campbell, M.J.: In-vitro anticancer activity of twelve Chinese medicinal herbs. Phytother. Res. 19, 649–651 (2005)
Svozil, D., Lohninger, H.: A program for the calculation of structural descriptors. TOPIX—A Program to Calculate Structural Descriptors 1999. https://www.lohninger.com/topix.html.
Singh, H., Rani, R., Mahajan, S.: Detection and classification of citrus leaf disease using hybrid features. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0751-9_67
Huang, X., Wang, P.: Chapter 4 Features and Features’ Selection for Medicinal Plants, 93–159 (2014)
Alamgir, A.N.M.: Progress in Drug Research, Volume 73: Therapeutic Use of Medicinal Plants and Their Extracts: Volume 1: Pharmacognosy, Vol. 1 (2017)
Jyothi, P.M.S., Nandan, D.: Utilization of the Internet of Things in agriculture: possibilities and challenges. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore (2020). http://doi-org-443.webvpn.fjmu.edu.cn/https://doi.org/10.1007/978-981-15-4032-5_75
Sihag, J., Prakash, D., Yadav, P.: Evaluation of soil physical, chemical parameter and enzyme activities as indicator of soil fertility with SFM Model in IA–AW Zone of Rajasthan. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H., (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 1154. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4032-5_98
Dewijanti, I.D., Mangunwardoyo, W., Artanti, N., Hanafi, M.: Bioactivities of Salam leaf (Syzygium polyanthum (Wight) Walp). In: AIP Conference Proceedings, 2168 (2019)
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Agrawal, S., Yellapragada, S. (2022). Automatic Identification of Medicinal Plants Using Morphological Features and Active Compounds. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_59
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