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Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Review Article

A Review on Robust Computational Approaches Based Identification and Authentication of Herbal Raw Drugs

Author(s): Preet Amol Singh, Neha Bajwa, Subh Naman and Ashish Baldi*

Volume 17, Issue 9, 2020

Page: [1066 - 1083] Pages: 18

DOI: 10.2174/1570180817666200304125520

Price: $65

Abstract

Background: Over the last decade, there has been a sudden rise in the demand for herbal as well as Information and Technology (IT) industry around the world. Identification of plant species has become useful and relevant to all the members of the society including farmers, traders, hikers, etc. Conventional authentication techniques such as morphological characterization, histological methods, and optical microscopy require multiple skills which are tedious, timeconsuming and difficult to learn for non-experts. This creates a hurdle for individuals interested in acquiring knowledge of species. Relying on rapid, economical and computerized approaches to identify and authenticate medicinal plants has become a recent development.

Objective: The purpose of this review is to summarize artificial intelligence-based technologies for wider dissemination of common plant-based knowledge such as identification and authentication to common people earlier limited to only experts.

Methods: A robust plant identification design enabling automated plant-organ and feature-based identification utilizing pattern recognition and image processing techniques resulting in image retrieval and recognition has been highlighted in this review for all the concerned stakeholders. Attempts have been made to compare conventional authentication methods with advanced computerized techniques to emphasize the advantages and future applications of an automated identification system in countering adulteration and providing fair trade opportunities to farmers.

Results: Major findings suggested that microscopical features such as shape and size of calcium oxalate crystals, trichomes, scleriods, stone cells, fibers, etc. are the essential descriptors for identification and authentication of herbal raw drugs using computational approaches.

Conclusion: This computational design can be successfully employed to address quality issues of medicinal plants. Therefore, computational techniques proved as a milestone in the growth of agriculture and medicinal plant industries.

Keywords: Authentication, artificial intelligence, biotechnology, computational techniques, herbal drugs, identification, medicinal plants, mobile applications, phyto-chemicals, phytopharmaceuticals, standardization.

Graphical Abstract
[1]
WHO Guidelines on Good Agricultural and Collection Practices (GACP) for Medicinal Plants; World Health Organization: Geneva, Switzerland, 2003.
[2]
Citarasu, T. Herbal biomedicines: A new opportunity for aquaculture industry. Aquaculture Intel., 2010, 18(403), 403-414.
[http://dx.doi.org/10.1007/s10499-009-9253-7]
[3]
Mukherjee, P.W. Quality Control of Herbal Drugs: An Approach to Evaluation of Botanicals; Business Horizons Publishers: New Delhi, India, 2002.
[4]
Bodeker, C.; Bodeker, G.; Ong, C.K. WHO Global Atlas of Traditional, Complementary and Alternative Medicine; World Health Organization: Geneva, Switzerland, 2005.
[5]
Ekor, M. The growing use of herbal medicines: Issues relating to adverse reactions and challenges in monitoring safety. Front. Pharmacol., 2014, 4(177), 177.
[http://dx.doi.org/10.3389/fphar.2013.00177] [PMID: 24454289]
[6]
Traditional Medicine Strategy (2002–2005). WHO/EDM/TRM/2002.1; World Health Organization: Geneva, Switzerland, 2002.
[7]
WHO Guidelines on Safety Monitoring of Herbal Medicines in Pharmacovigilance Systems; World Health Organization: Geneva, Switzerland, 2004.
[8]
National Policy on Traditional Medicine and Regulation of Herbal Medicines. Report of a World Health Organization Global Survey; Geneva, Switzerland, 2005.
[9]
Goraya, G. S.; Ved, D. K. Medicinal Plants in India: An Assessment of their Demand and Supply. National Medicinal Plants Board, Ministry of AYUSH, Government of India, New-Delhi and Indian Council of Forestry Research & Education, Dehradun.,, 2017, 1-307.
[10]
Hamilton, A.C. Medicinal plants, conservation and livelihoods. Biodivers. Conserv., 2004, 13(8), 1477-1517.
[http://dx.doi.org/10.1023/B:BIOC.0000021333.23413.42]
[11]
Wiersum, K.F.; Dold, A.P.; Husselman, M. Cultivation of medicinal plants as a tool for biodiversity con-servation and poverty alleviation in the Amatola region, South Africa. Frontis; Springer: Netherlands, 2006, pp. 43-57.
[12]
Dubey, K.; Dubey, K. Biodiversity conservation of medicinal plants. J. Res. Educ. Indian Med., 2011, 17(1-2), 1-6.
[13]
Sharma, V.; Sarkar, I.N. Bioinformatics opportunities for identification and study of medicinal plants. Brief. Bioinform., 2013, 14(2), 238-250.
[http://dx.doi.org/10.1093/bib/bbs021] [PMID: 22589384]
[14]
Luo, D.; Fan, D.; Yu, H. A new processing technique for the identification of Chinese herbal medicine. 2013Fifth International Conference on Computational and Information Sciences (ICCIS), Shiyang, China, pp. 474-477.
[15]
Everstine, K.; Spink, J.; Kennedy, S. Economically motivated adulteration (EMA) of food: Common characteristics of EMA incidents. J. Food Prot., 2013, 76(4), 723-735.
[http://dx.doi.org/10.4315/0362-028X.JFP-12-399] [PMID: 23575142]
[16]
Liu, C.; Wu, X.; Xiong, W. Chinese herbal medicine classification based on BP neural network. J. Softw., 2014, 9(4), 938-944.
[17]
Norazian, S. Development of intelligent classifier and estimator for tualang honey purity., Doctoral dissertation, Universiti Sains Malaysia, . 2014.
[18]
Liang, Y.Z.; Xie, P.; Chan, K. Quality control of herbal medicines. J. Chromatogr. B Anal. Technol. Biomed. Life Sci., 2004, 812(1-2), 53-70.
[http://dx.doi.org/10.1016/S1570-0232(04)00676-2] [PMID: 15556488]
[19]
Techen, N.; Crockett, S.L.; Khan, I.A.; Scheffler, B.E. Authentication of medicinal plants using molecular biology techniques to compliment conventional methods. Curr. Med. Chem., 2004, 11(11), 1391-1401.
[http://dx.doi.org/10.2174/0929867043365206] [PMID: 15180573]
[20]
Gurib-Fakim, A. Medicinal plants: Traditions of yesterday and drugs of tomorrow. Mol. Aspects Med., 2006, 27(1), 1-93.
[http://dx.doi.org/10.1016/j.mam.2005.07.008] [PMID: 16105678]
[21]
Sahoo, N.; Manchikanti, P.; Dey, S. Herbal drugs: Standards and regulation. Fitoterapia, 2010, 81(6), 462-471.
[http://dx.doi.org/10.1016/j.fitote.2010.02.001] [PMID: 20156530]
[22]
Zhang, Y.; Wu, L. Classification of fruits using computer vision and a multiclass support vector machine. Sensors (Basel), 2012, 12(9), 12489-12505.
[http://dx.doi.org/10.3390/s120912489] [PMID: 23112727]
[23]
Joly, A.; Goëau, H.; Bonnet, P. Interactive plant identification based on social image data. Ecol. Inform., 2014, 23, 22-34.
[http://dx.doi.org/10.1016/j.ecoinf.2013.07.006]
[24]
Chang, E.; Chang, S.F.; Hauptmann, A.G. Web-scale multimedia processing and applications. Proc. IEEE, 2012, 100(9), 2580-2583.
[http://dx.doi.org/10.1109/JPROC.2012.2204110]
[25]
Wäldchen, J.; Mäder, P. Plant species identification using computer vision techniques: A systematic literature review. Arch. Comput. Methods Eng., 2018, 25(2), 507-543.
[http://dx.doi.org/10.1007/s11831-016-9206-z] [PMID: 29962832]
[26]
Heubl, G. New aspects of DNA-based authentication of Chinese medicinal plants by molecular biological techniques. Planta Med., 2010, 76(17), 1963-1974.
[http://dx.doi.org/10.1055/s-0030-1250519] [PMID: 21058240]
[27]
Korir, N.K.; Han, J.; Shangguan, L.; Wang, C.; Kayesh, E.; Zhang, Y.; Fang, J. Plant variety and cultivar identification: Advances and prospects. Crit. Rev. Biotechnol., 2013, 33(2), 111-125.
[http://dx.doi.org/10.3109/07388551.2012.675314] [PMID: 22698516]
[28]
Chen, S.; Pang, X.; Song, J.; Shi, L.; Yao, H.; Han, J.; Leon, C. A renaissance in herbal medicine identification: From morphology to DNA. Biotechnol. Adv., 2014, 32(7), 1237-1244.
[http://dx.doi.org/10.1016/j.biotechadv.2014.07.004] [PMID: 25087935]
[29]
Zhang, Y.B.; But, P.P.H.; Wang, Z.T. Current approaches for the authentication of medicinal Dendrobium species and its products. Plant Genet. Resour., 2005, 3(2), 144-148.
[http://dx.doi.org/10.1079/PGR200578]
[30]
Sucher, N.J.; Carles, M.C. Genome-based approaches to the authentication of medicinal plants. Planta Med., 2008, 74(06), 603-623.
[31]
Chanda, S. Importance of pharmacognostic study of medicinal plants: An overview. J. Pharmacog. Phytochem., 2014, 2(5), 69-73.
[32]
Gaston, K.J.; O’Neill, M.A. Automated species identification: Why not? Philos. Trans. R. Soc. Lond. B Biol. Sci., 2004, 359(1444), 655-667.
[http://dx.doi.org/10.1098/rstb.2003.1442] [PMID: 15253351]
[33]
Nilsson, N.J. A mobile automaton: An application of artificial intelligence techniques; Sri International Menlo Park Ca Artificial Intelligence Center, 1969.
[http://dx.doi.org/10.21236/ADA459660]
[34]
Kotsiantis, S.B.; Zaharakis, I.; Pintelas, P. Supervised machine learning: A review of classification techniques. Emer. Art. Int. Appl. Comp. Eng., 2007, 160, 3-24.
[35]
Lorena, A.C.; De Carvalho, A.C.; Gama, J.M. A review on the combination of binary classifiers in multiclass problems. Artif. Intell. Rev., 2008, 30(1-4), 19-37.
[http://dx.doi.org/10.1007/s10462-009-9114-9]
[36]
Zhao, Z.; Hu, Y.; Liang, Z. Authentication is fundamental for standardization of Chinese medicines. Plan med.,, 2006, 72(10), 865-874.
[37]
Zhang, Y.B.; Shaw, P.C.; Sze, C.W. Molecular authentication of Chinese herbal materials. Yao Wu Shi Pin Fen Xi, 2007, 15(1), 1-9.
[38]
Folashade, O.; Omoregie, H.; Ochogu, P. Standardization of herbal medicines-a review. Int. J. Biodivers. Conserv., 2012, 4(3), 101-112.
[http://dx.doi.org/10.5897/IJBC11.163]
[39]
Joshi, K.; Chavan, P.; Warude, D. Molecular markers in herbal drug technology. Curr. Sci., 2004, 87(2), 159-165.
[40]
Yadav, N.P.; Dixit, V.K. Recent approaches in herbal drug standardization. Int. J. Integr. Biol., 2008, 2(3), 195-203.
[41]
Frankel, O.H.; Brown, A.H.; Burdon, J.J. The Conservation of Plant Biodiversity; Cambridge University Press, 1995.
[42]
Rivera, D.; Allkin, R.; Obón, C.; Alcaraz, F.; Verpoorte, R.; Heinrich, M. What is in a name? The need for accurate scientific nomenclature for plants. J. Ethnopharmacol., 2014, 152(3), 393-402.
[http://dx.doi.org/10.1016/j.jep.2013.12.022] [PMID: 24374235]
[43]
Sun, Y.; Liu, Y.; Wang, G. Deep learning for plant identification in natural environment. Comp. Int. Neurosci. , 2017, 1-6.
[44]
Li, T.; Zhang, H. Application of microscopy in authentication of traditional Tibetan medicinal plants of five Rhodiola (Crassulaceae) alpine species by comparative anatomy and micromorphology. Microsc. Res. Tech., 2008, 71(6), 448-458.
[http://dx.doi.org/10.1002/jemt.20570] [PMID: 18300292]
[45]
Singh, P.A.; Desai, S.D.; Singh, J. A review on plant antimicrobials of past decade. Curr. Top. Med. Chem., 2018, 18(10), 812-833.
[http://dx.doi.org/10.2174/1568026618666180516123229] [PMID: 29766808]
[46]
Anonymous, The Ayurvedic Pharmacopoeia of India, Part-I In: Government of India, Ministry of Health and Family Welfare, Department of Health, New Delhi, India ; 1st English ed.; . , 1989.
[47]
The United States Pharmacopeia. 30th Revision/National Formulary, 25th ed; The United States Pharmacopeial Convention: Rochville, 2005.
[48]
The Japanese Pharmacopoeia, 15th ed; Society of Japanese Pharmacopoeia: Tokyo, 2006.
[49]
Vietnamese Pharmacopoeia. 2005.
[50]
Singh, D.; Aeri, V.; Ananthanarayana, D.B. Development of standard operating protocol for slide preparation of powdered bark samples with varying grinding techniques. Pharmacogn. J., 2018, 10(2), 265-271.
[http://dx.doi.org/10.5530/pj.2018.2.47]
[51]
Jackson, B.P.; Snowdon, D.W. Atlas of Microscopy of Medicinal Plants, Culinary Herbs and Spices; Belhaven Press, 1990.
[52]
Sultana, S.; Khan, M.A.; Ahmad, M. Authentication of herbal medicine neem (Azadirachta indica A. Juss.) by using taxonomic and pharmacognostic techniques. Pak. J. Bot., 2011, 43, 141-150.
[53]
Yadav, R.N.S.; Agarwala, M. Phytochemical analysis of some medicinal plants. J. Phytol., 2011, 3(12), 10-14.
[54]
Mir, M.A.; Sawhney, S.S.; Jassal, M.M.S. Qualitative and quantitative analysis of phytochemicals of Taraxacum officinale. Wudpec J. Pharm. Pharmocol., 2013, 2(1), 1-5.
[55]
Edeoga, H.O.; Okwu, D.E.; Mbaebie, B.O. Phytochemical constituents of some Nigerian medicinal plants. Afr. J. Biotechnol., 2005, 4(7), 685-688.
[http://dx.doi.org/10.5897/AJB2005.000-3127]
[56]
Wojdyło, A.; Oszmiański, J.; Czemerys, R. Antioxidant activity and phenolic compounds in 32 selected herbs. Food Chem., 2007, 105(3), 940-949.
[http://dx.doi.org/10.1016/j.foodchem.2007.04.038]
[57]
Eckert, C.G.; Samis, K.E.; Lougheed, S.C. Genetic variation across species’ geographical ranges: The central-marginal hypothesis and beyond. Mol. Ecol., 2008, 17(5), 1170-1188.
[http://dx.doi.org/10.1111/j.1365-294X.2007.03659.x] [PMID: 18302683]
[58]
Guillon, S.; Trémouillaux-Guiller, J.; Pati, P.K.; Rideau, M.; Gantet, P. Hairy root research: recent scenario and exciting prospects. Curr. Opin. Plant Biol., 2006, 9(3), 341-346.
[http://dx.doi.org/10.1016/j.pbi.2006.03.008] [PMID: 16616871]
[59]
McChesney, J.D.; Venkataraman, S.K.; Henri, J.T. Plant natural products: Back to the future or into extinction? Phytochemistry, 2007, 68(14), 2015-2022.
[http://dx.doi.org/10.1016/j.phytochem.2007.04.032] [PMID: 17574638]
[60]
Kumar, S.; Pandey, A.K. Chemistry and biological activities of flavonoids: An overview. ScientificWorldJournal, 2013.Article ID 162750
[61]
Khan, S.; Qureshi, M.I.; Alam, T. Protocol for isolation of genomic DNA from dry and fresh roots of medicinal plants suitable for RAPD and restriction digestion. Afr. J. Biotechnol., 2007, 6(3), 175-178.
[http://dx.doi.org/10.5897/AJB06.612]
[62]
Akopyanz, N.; Bukanov, N.O.; Westblom, T.U.; Kresovich, S.; Berg, D.E. DNA diversity among clinical isolates of Helicobacter pylori detected by PCR-based RAPD fingerprinting. Nucleic Acids Res., 1992, 20(19), 5137-5142.
[http://dx.doi.org/10.1093/nar/20.19.5137] [PMID: 1408828]
[63]
Powell, W.; Morgante, M.; Andre, C. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol. Breed., 1996, 2(3), 225-238.
[http://dx.doi.org/10.1007/BF00564200]
[64]
Rohit, A.; Maiti, B.; Shenoy, S.; Karunasagar, I. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) for rapid diagnosis of neonatal sepsis. Indian J. Med. Res., 2016, 143(1), 72-78.
[http://dx.doi.org/10.4103/0971-5916.178613] [PMID: 26997017]
[65]
Vos, P.; Hogers, R.; Bleeker, M.; Reijans, M.; van de Lee, T.; Hornes, M.; Frijters, A.; Pot, J.; Peleman, J.; Kuiper, M. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Res., 1995, 23(21), 4407-4414.
[http://dx.doi.org/10.1093/nar/23.21.4407] [PMID: 7501463]
[66]
Mastan, S.G.; Rathore, M.S.; Ghosh, A. Molecular characterization of genetic and epigenetic divergence in selected Jatropha curcas L. germplasm using AFLP and MS-AFLP markers. Plant Gene, 2016, 8, 42-49.
[http://dx.doi.org/10.1016/j.plgene.2016.10.001]
[67]
Qu, Y.; Yu, H. Genetic diversity and population structure of the endangered species Psammosilene tunicoides revealed by DALP analysis. Biochem. Syst. Ecol., 2010, 38(5), 880-887.
[http://dx.doi.org/10.1016/j.bse.2010.09.007]
[68]
Ganie, S.H.; Upadhyay, P.; Das, S.; Prasad Sharma, M. Authentication of medicinal plants by DNA markers. Plant Gene, 2015, 4, 83-99.
[http://dx.doi.org/10.1016/j.plgene.2015.10.002] [PMID: 32289060]
[69]
Liu, Z.Y.; Song, S.S.; Huo, Z.S.; Song, X.C.; Cong, B.; Yang, F.H. Detection of self-biting behavior of mink by loop-mediated isothermal amplification (LAMP) and sequence-characterized amplified regions (SCAR). Pol. J. Vet. Sci., 2018, 21(2), 371-376.
[PMID: 30450877]
[70]
Rychlik, W.; Spencer, W.J.; Rhoads, R.E. Optimization of the annealing temperature for DNA amplification in vitro. Nucleic Acids Res., 1990, 18(21), 6409-6412.
[http://dx.doi.org/10.1093/nar/18.21.6409] [PMID: 2243783]
[71]
Pavlov, A.R.; Pavlova, N.V.; Kozyavkin, S.A.; Slesarev, A.I. Recent developments in the optimization of thermostable DNA polymerases for efficient applications. Trends Biotechnol., 2004, 22(5), 253-260.
[http://dx.doi.org/10.1016/j.tibtech.2004.02.011] [PMID: 15109812]
[72]
Fittipaldi, M.; Codony, F.; Adrados, B.; Camper, A.K.; Morató, J. Viable real-time PCR in environmental samples: Can all data be interpreted directly? Microb. Ecol., 2011, 61(1), 7-12.
[http://dx.doi.org/10.1007/s00248-010-9719-1] [PMID: 20632000]
[73]
Belhumeur, P.N.; Chen, D.; Feiner, S. Searching the world’s herbaria: A system for visual identification of plant species. European Conference on Computer Vision, Lecture Notes in Computer Science, 2008, 5305 , pp. 116-129.
[http://dx.doi.org/10.1007/978-3-540-88693-8_9]
[74]
Wang, B.; Brown, D.; Gao, Y. Mobile. International Conference on Image Processing (ICIP), 20th IEEE , Melbourne, . 2013, pp. 4417-4421.
[75]
Urbanowicz, R.J.; Moore, J.H. Learning classifier systems: A complete introduction, review, and roadmap. J. Art. Evo. Appl., 2009, 72, 1-25.
[http://dx.doi.org/10.1155/2009/736398]
[76]
Learning classifier systems: From foundations to applications (No. 1813).. Lecture notes in Computer Science ; Springer Science & Business Media, 2000, pp. 3-349.
[http://dx.doi.org/10.1007/3-540-45027-0]
[77]
Bull, L.; Lanzi, P.L.; Stolzmann, W. Learning classifier systems. Soft computing- A fusion of fFoundations. Methodol. Appl., 2002, 6(143), 143-143.
[http://dx.doi.org/10.1007/s005000100110]
[78]
Sun, X.; Qian, H. Chinese herbal medicine image recognition and retrieval by convolutional neural network. PLoS One, 2016, 11(6)e0156327
[http://dx.doi.org/10.1371/journal.pone.0156327] [PMID: 27258404]
[79]
Kan, H.X.; Jin, L.; Zhou, F.L. Classification of medicinal plant leaf image based on multi-feature extraction. Patt Reco. Imag. Anal., 2017, 27(3), 581-587.
[http://dx.doi.org/10.1134/S105466181703018X]
[80]
Du, J.X.; Huang, D.S.; Wang, X.F. Computer-aided plant species identification (CAPSI) based on leaf shape matching technique. Trans. Inst. Meas. Contr., 2006, 28(3), 275-285.
[http://dx.doi.org/10.1191/0142331206tim176oa]
[81]
Goëau, H.; Bonnet, P.; Joly, A. Pl@ntnet mobile 2014: Android port and new features. ICMR, 2014; pp. 527-530.
[http://dx.doi.org/10.1145/2578726.2582618f]
[82]
Kebapci, H.; Yanikoglu, B.; Unal, G. Plant image retrieval using color and texture features. Comput. J. Adv. Access, 2009, 82-87.
[83]
Kebapci, H.; Yanikoglu, B.; Unal, G. Plant image retrieval using color, shape and texture features. Comput. J., 2011, 54(9), 1475-1490.
[http://dx.doi.org/10.1093/comjnl/bxq037]
[84]
Teng, C.H.; Kuo, Y.T.; Chen, Y.S. Leaf segmentation, its 3d position estimation and leaf classification from a few images with very close viewpoints. International Conference Image Analysis and Recognition, Image Analysis and Recognition, Lecture Notes in Computer Science, 2009, 5627, pp. 937-946.
[http://dx.doi.org/10.1007/978-3-642-02611-9_92]
[85]
Nesaratnam, J. Identifying leaf in a natural image using morphological characters. 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2015, pp. 1-5.
[http://dx.doi.org/10.1109/ICIIECS.2015.7193115]
[86]
Nilsback, M.E.; Zisserman, A. Automated flower classification over a large number of classes. Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India2008, pp. 722-729.
[http://dx.doi.org/10.1109/ICVGIP.2008.47]
[87]
Qi, W.; Liu, X.; Zhao, J. Flower classification based on local and spatial visual cues. 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Vol. 3, Zhangjiajie, China2012, , pp. 670-674.
[http://dx.doi.org/10.1109/CSAE.2012.6273040]
[88]
Yanikoglu, B.; Aptoula, E.; Tirkaz, C. Automatic plant identification from photographs. Mach. Vis. Appl., 2014, 25(6), 1369-1383.
[http://dx.doi.org/10.1007/s00138-014-0612-7]
[89]
Prasad, S.; Kudiri, K.M.; Tripathi, R.C. Relative sub-image based features for leaf recognition using support vector machine. Proceedings of the 2011 International Conference on Communication, Computing & Security, Rourkela, Odisha, India2011, pp. 343-346.
[http://dx.doi.org/10.1145/1947940.1948012]
[90]
Gonzalez, R.; Woods, R. Digital Image Processing, 3rd ed .
[91]
Xiaofeng, W.; Deshuang, H.; Jixiang, D.U. Feature extraction and recognition for leaf images. Comp. Eng. App., 2006, 42(3), 190-193.
[92]
Santana, F.S.; Costa, A.H.R.; Truzzi, F.S. A reference process for automating bee species identification based on wing images and digital image processing. Ecol. Inform., 2014, 24, 248-260.
[http://dx.doi.org/10.1016/j.ecoinf.2013.12.001]
[93]
Arun, C.H.; Emmanuel, W.S.; Durairaj, D.C. Texture feature extraction for identification of medicinal plants and comparison of different classifiers. Int. J. Comput. Appl., 2013, 62(12), 1-9.
[94]
Yanikoglu, B.A.; Aptoula, E.; Tirkaz, C. Sabanci-Okan System at Image Clef 2012: Combining Features and Classifiers for Plant Identification; CLEF Online Working Notes/Labs/Workshop, 2012, pp. 1-13.
[95]
Kho, S.J.; Manickam, S.; Malek, S. Automated plant identification using artificial neural network and support vector machine. Front. Life Sci., 2017, 10(1), 98-107.
[http://dx.doi.org/10.1080/21553769.2017.1412361]
[96]
Chaki, J.; Parekh, R.; Bhattacharya, S. Plant leaf classification using multiple descriptors: A hierarchical approach. J. King Saud Univer. Comp. Infor. Sci., 2018, 72(10), 4417-4421.
[http://dx.doi.org/10.1016/j.jksuci.2018.01.007]
[97]
Zhang, S.; Zhang, C.; Wang, Z.; Kong, W. Combining sparse representation and singular value decomposition for plant recognition. Appl. Soft Comput., 2018, 67, 164-171.
[http://dx.doi.org/10.1016/j.asoc.2018.02.052]
[98]
Ghazi, M.M.; Yanikoglu, B.; Aptoula, E. Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing, 2017, 235, 228-235.
[http://dx.doi.org/10.1016/j.neucom.2017.01.018]
[99]
Grinblat, G.L.; Uzal, L.C.; Larese, M.G. Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric., 2016, 127, 418-424.
[http://dx.doi.org/10.1016/j.compag.2016.07.003]
[100]
Dyrmann, M.; Karstoft, H.; Midtiby, H.S. Plant species classification using deep convolutional neural network. Biosyst. Eng., 2016, 151, 72-80.
[http://dx.doi.org/10.1016/j.biosystemseng.2016.08.024]
[101]
Jamil, N.; Hussin, N.A.C.; Nordin, S. automatic plant identification: Is shape the key feature? Procedia Comput. Sci., 2015, 76, 436-442.
[http://dx.doi.org/10.1016/j.procs.2015.12.287]
[102]
Raji, I.K.; Thyagharajan, K.K. An analysis of segmentation techniques to identify herbal leaves from complex background. Procedia Comput. Sci., 2015, 48, 589-599.
[http://dx.doi.org/10.1016/j.procs.2015.04.140]
[103]
Jobin, A.; Nair, M.S.; Tatavarti, R. Plant identification based on fractal refinement technique(FRT). Proced Technol., 2012, 6, 171-179.
[http://dx.doi.org/10.1016/j.protcy.2012.10.021]
[104]
Phadikar, S.; Sil, J.; Das, A.K. Rice diseases classification using feature selection and rule generation techniques. Comput. Electron. Agric., 2013, 90, 76-85.
[http://dx.doi.org/10.1016/j.compag.2012.11.001]
[105]
Pujari, J.D.; Yakkundimath, R.; Byadgi, A.S. Image processing based detection of fungal diseases in plants. Procedia Comput. Sci., 2015, 46, 1802-1808.
[http://dx.doi.org/10.1016/j.procs.2015.02.137]
[106]
Anami, B.S.; Nandyal, S.S.; Govardhan, A. A combined color, texture and edge features based approach for identification and classification of Indian medicinal plants. Int. J. Comput. Appl., 2010, 6(12), 45-51.
[http://dx.doi.org/10.5120/1122-1471]
[107]
Barré, P.; Stöver, B.C.; Müller, K.F. Leaf net: A computer vision system for automatic plant species identification. Ecol. Inform., 2017, 40, 50-56.
[http://dx.doi.org/10.1016/j.ecoinf.2017.05.005]
[108]
Zhao, Z.Q.; Ma, L.H.; Cheung, Y.M. Ap Leaf: An efficient android-based plant leaf identification system. Neurocomputing, 2015, 151, 1112-1119.
[109]
Prasvita, D.S.; Herdiyeni, Y. Medleaf: Mobile application for medicinal plant identification based on leaf image. Inter. J. Advan. Sci. Eng. Infor. Tech., 2013, 3(2), 103-106.
[http://dx.doi.org/10.18517/ijaseit.3.2.287]
[110]
Munisami, T.; Ramsurn, M.; Kishnah, S. Plant leaf recognition using shape features and colour histogram with K-nearest neighbour classifiers. Procedia Comput. Sci., 2015, 58, 740-747.
[http://dx.doi.org/10.1016/j.procs.2015.08.095]
[111]
Zhang, Y.; Li, B. Wild plant data collection system based on distributed location. J. Comput. Sci., 2018, 28, 389-397.
[http://dx.doi.org/10.1016/j.jocs.2017.04.013]
[112]
Kumar, N.; Belhumeur, P.N.; Biswas, A. Leafsnap: A computer vision system for automatic plant species identification; , 2012.
[113]
Cerutti, G.; Tougne, L.; Mille, J. Understanding leaves in natural images-A model-based approach for tree species identification. Comput. Vis. Image Underst., 2013, 117(10), 1482-1501.
[http://dx.doi.org/10.1016/j.cviu.2013.07.003]
[114]
Herdiyeni, Y.; Wahyuni, N.K.S. Mobile International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2012, pp. 301-306.
[115]
Kim, S.T.; Lee, S.Y.; Kim, S.C. Development of a mobile application. J. Asia-Pac. Biodivers., 2011, 4(3), 139-150.
[http://dx.doi.org/10.7229/jkn.2011.4.3.139]
[116]
Hansen, M.; Dubayah, R.; DeFries, R. Classification trees: An alternative to traditional land cover classifiers. Int. J. Remote Sens., 1996, 17(5), 1075-1081.
[http://dx.doi.org/10.1080/01431169608949069]
[117]
Ghasab, M.A.J.; Khamis, S.; Mohammad, F. Feature decision-making ant colony optimization system for an automated recognition of plant species. Expert Syst. Appl., 2015, 42(5), 2361-2370.
[http://dx.doi.org/10.1016/j.eswa.2014.11.011]
[118]
Suchacz, B.; Wesolowski, M. Herbal drug raw materials differentiation by neural networks using non-metals content. Cent. Eur. J. Chem., 2010, 8(6), 1298-1304.
[http://dx.doi.org/10.2478/s11532-010-0105-0]
[119]
Aakif, A.; Khan, M.F. Automatic classification of plants based on their leaves. Biosyst. Eng., 2015, 139, 66-75.
[http://dx.doi.org/10.1016/j.biosystemseng.2015.08.003]
[120]
Cerutti, G.; Tougne, L.; Mille, J. A model-based approach for compound leaves understanding and identification. 2013.
[121]
Chaki, J.; Parekh, R.; Bhattacharya, S. Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recognit. Lett., 2015, 58, 61-68.
[http://dx.doi.org/10.1016/j.patrec.2015.02.010]
[122]
Chen, Y.; Lin, P.; He, Y. Velocity representation method for description of contour shape and the classification of weed leaf images. Biosyst. Eng., 2011, 109(3), 186-195.
[http://dx.doi.org/10.1016/j.biosystemseng.2011.03.004]
[123]
Goëau, H.; Joly, A.; Bonnet, P. 2013.
[124]
Zhao, C.; Chan, S.S.; Cham, W.K. Plant identification using leaf shapes-A pattern counting approach. Patt. Recog., 2015, 48(10), 3203-3215.
[http://dx.doi.org/10.1016/j.patcog.2015.04.004]
[125]
Şekeroğlu, B.; İnan, Y. Leaves recognition system using a neural network. Procedia Comput. Sci., 2016, 102, 578-582.
[http://dx.doi.org/10.1016/j.procs.2016.09.445]
[126]
Tharwat, A.; Gaber, T.; Hassanien, A.E. One-dimensional vs. two-dimensional based features: Plant identification approach. J. Appl. Log., 2017, 24, 15-31.
[http://dx.doi.org/10.1016/j.jal.2016.11.021]
[127]
Foggia, P.; Sansone, C.; Vento, M. 200915th International Conference Vietri sul Mare, , pp. 8-11.
[128]
Casanova, D.; de Mesquita Sá, J.J. Junior; Bruno, O.M. Plant leaf identification using Gabor wavelets. Inter. J. Imag. Sys. Tech., 2009, 19(3), 236-243.
[http://dx.doi.org/10.1002/ima.20201]
[129]
Bebis, G.; Boyle, R.; Parvin, B.
[130]
Charters, J.; Wang, Z.; Chi, Z. Eagle: A novel descriptor for identifying plant species using leaf lamina vascular features. IEEE Intl. Conf., 2014, pp. 1-6.
[131]
Prasad, S.; Kumar, P.; Tripathi, R.C. 2011.
[132]
A shape-based retrieval scheme for leaf images. Advances in Multimedia Information Processing-PCM; Nam, Y.; Hwang, E. Lecture notes in Computer ScienceSpringer: Berlin, Heidelberg, 2005.
[133]
Rashad, M.Z.; El-Desouky, B.S.; Khawasik, M.S. Plants images classification based on textural features using combined classifier. Int. J. Comp. Sci. Infor. Tech., 2011, 3(4), 93-100.
[http://dx.doi.org/10.5121/ijcsit.2011.3407]
[134]
Pham, N.H.; Le, T.L.; Grard, P. Computer aided plant identification system. 2013 International Conference on Computing, Management and Telecommunications (ComManTel), 2013, pp. 134-139.
[135]
Rejeb Sfar, A.; Boujemaa, N.; Geman, D. Identification of plants from multiple images and botanical idkeys. Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, New York, USA2013, , pp. 191-198.
[136]
Venkatesh, S.K.; Raghavendra, R. Local gabor phase quantization scheme for robust leaf classification. 2011Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Hubli, Karnataka, India2011, pp. 211-214.
[137]
Wang, X.F.; Du, J.X.; Zhang, G.J. Recognition of leaf images based on shape features using a hypersphere classifier. 2005.
[138]
Zhai, C.M.; Du, J.X. Applying extreme learning machine to plant species identification. International Conference on Information and Automation, Changsha, China2008, pp. 879-884.
[139]
Gu, X.; Du, J.X.; Wang, X.F. Leaf recognition based on the combination of wavelet transform and gaussian interpolation.
[140]
Hussin, N.A.C.; Jamil, N.; Nordin, S. Plant species identification by using scale invariant feature transform (sift) and grid based colour moment (gbcm). IEEE Conference on Open Systems (ICOS), Kuching, Malaysia2013, pp. 226-230.
[141]
Wei, Q.; Chui, Y.H.; Leblon, B. Identification of selected internal wood characteristics in computed tomography images of black spruce: A comparison study. J. Wood Sci., 2009, 55(3), 175-180.
[http://dx.doi.org/10.1007/s10086-008-1013-1]
[142]
Huang, Z.K.; Wang, Z.F. Bark classification using RBPNN in different color space. Neu. Infor. Proc. Lett. Revi., 2007, 11(1), 7-13.
[143]
Boudra, S.; Yahiaoui, I.; Behloul, A. , 2017.
[144]
Wendel, A.; Sternig, S.; Godec, M. Automated identification of tree species from images of the bark, leaves and needles. 16th Computer Vision Winter Workshop, 2011, pp. 67-70.
[145]
Tan, W.N.; Tan, Y.F.; Koo, A.C. Petals’ shape descriptor for blooming flowers recognition. Fourth International Conference, 2012.
[146]
Tan, W.N.; Sem, R.; Tan, Y.F. Blooming flower recognition by using eigen values of shape features. Sixth International Conference on Digital Image Processing, 2014.
[147]
Cho, S.Y.; Lim, P.T. A novel virus infection clustering for flower images identification. 18th International Conference on Pattern Recognition, 2006, pp. 1038-1041.
[148]
Pardee, W.; Yusungnern, P.; Sripian, P. Flower Identification System by Image Processing. 3rd International Conference on Creative Technology CRETECH, 2015, Vol. 1, pp. 1-4.
[149]
Muhammad Ashraq, S. Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks., 2013.
[150]
Apriyanti, D.H.; Arymurthy, A.M.; Handoko, L.T. Identification of orchid species using content-based flower image retrieval. 2013.
[151]
Zawbaa, H.M.; Abbass, M.; Basha, S.H. An automatic flower classification approach using machine learning algorithms. International Conference on Advances in Computing, Communications and Informatics (ICACCI), , pp. 895-901.
[152]
Nilsback, M.E.; Zisserman, A. A visual vocabulary for flower classification. 2006.
[153]
Jiménez, A.R.; Jain, A.K.; Ceres, R. Automatic fruit recognition: A survey and new results using range/attenuation images. Patt Recog., 1999, 32(10), 1719-1736.
[http://dx.doi.org/10.1016/S0031-3203(98)00170-8]
[154]
Song, Y.; Glasbey, C.A.; Horgan, G.W. Automatic fruit recognition and counting from multiple images. Biosyst. Eng., 2014, 118, 203-215.
[http://dx.doi.org/10.1016/j.biosystemseng.2013.12.008]
[155]
Arivazhagan, S.; Shebiah, R.N.; Nidhyanandhan, S.S. Fruit recognition using color and texture features. J. Emer. Trend. Comp. Infor. Sci., 2010, 1(2), 90-94.
[156]
Zhang, Y.; Wang, S.; Ji, G. Fruit classification using computer vision and feed forward neural network. J. Food Eng., 2014, 143, 167-177.
[http://dx.doi.org/10.1016/j.jfoodeng.2014.07.001]
[157]
Jimenez, A.R.; Ceres, R.; Pons, J.L. A survey of computer vision methods for locating fruit on trees. Trans. ASAE, 2000, 43(6), 1911-1920.
[http://dx.doi.org/10.13031/2013.3096]
[158]
Holalad, H.; Warrier, P.; Sabarad, A. An FPGA based efficient fruit recognition system using minimum distance classifier. J. Inf. Eng. Appl., 2012, 2(6), 1-10.
[159]
Unay, D.; Gosselin, B. Artificial neural network-based segmentation and apple grading by machine vision 2005.
[160]
Ji, W.; Zhao, D.; Cheng, F. Automatic recognition vision system guided for apple harvesting robot. Comput. Electr. Eng., 2012, 38(5), 1186-1195.
[http://dx.doi.org/10.1016/j.compeleceng.2011.11.005]
[161]
Mitra, S.K.; Kannan, R. A note on unintentional adulterations in Ayurvedic herbs. Ethnobotan Leaflet., 2007, 2007(1), 11-15.
[162]
Evans, W.C. Trease and Evans’ Pharmacognosy E-Book; Elsevier Health Sciences, 2009.
[163]
Meng, F.C.; Zhou, Y.Q.; Ren, D. Turmeric: A review of its chemical composition, quality control, bioactivity, and pharmaceutical application. Natural and Artificial Flavoring Agents and Food Dyes, 2018, 7, 299-350.
[http://dx.doi.org/10.1016/B978-0-12-811518-3.00010-7]]
[164]
Singhal, R.S.; Kulkarni, P.K.; Rege, D.V. Handbook of indices of food quality and authenticity; Woodhead Publishing Limited, 1997.
[http://dx.doi.org/10.1533/9781855736474]
[165]
Beristain, C.I.; Garcıa, H.S.; Vernon-Carter, E.J. Spray-dried encapsulation of cardamom (Elettaria cardamomum) essential oil with mesquite (Prosopis juliflora) gum. LWT-Food Sci and Tech., 2001, 34(6), 398-401.
[http://dx.doi.org/10.1006/fstl.2001.0779]
[166]
Zhu, H.; Zhao, M. Study on the microscopic identification of the adulterated plant origin powdered seasonings. Discour. J. Agr. Food Sci., 2014, 2(9), 264-269.
[167]
Bishr, M.M.; Haggag, E.G.; Moawed, M.M. Characterization of fennel fruits: Types and quality (I). Life Sci. J., 2012, 9(2), 686-691.
[168]
Ferentinos, K.P. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric., 2018, 145, 311-318.
[http://dx.doi.org/10.1016/j.compag.2018.01.009]
[169]
Dey, A.K.; Sharma, M.; Meshram, M.R. Image processing based leaf rot disease, detection of betel vine (Piper Betle L.). Procedia Comput. Sci., 2016, 85, 748-754.
[http://dx.doi.org/10.1016/j.procs.2016.05.262]
[170]
Hunt, R.; Causton, D.R.; Shipley, B.; Askew, A.P. A modern tool for classical plant growth analysis. Ann. Bot., 2002, 90(4), 485-488.
[http://dx.doi.org/10.1093/aob/mcf214] [PMID: 12324272]
[171]
Tessmer, O.L.; Jiao, Y.; Cruz, J.A.; Kramer, D.M.; Chen, J. Functional approach to high-throughput plant growth analysis. BMC Syst. Biol., 2013, 7(6)(Suppl. 6), S17.
[http://dx.doi.org/10.1186/1752-0509-7-S6-S17] [PMID: 24565437]
[172]
Mourtzis, D.; Doukas, M.; Vandera, C. Mobile apps for product customisation and design of manufacturing networks. Manuf. Lett., 2014, 2(2), 30-34.
[http://dx.doi.org/10.1016/j.mfglet.2014.01.002]

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