Skip to main content
SpringerLink
Log in

Machine learning in medicinal plants recognition: a review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Medicinal plants are gaining attention in the pharmaceutical industry due to having less harmful effects reactions and cheaper than modern medicine. Based on these facts, many researchers have shown considerable interest in the research of automatic medicinal plants recognition. There are various opportunities for advancement in producing a robust classifier that has the ability to classify medicinal plants accurately in real-time. In this paper, various effective and reliable machine learning algorithms for plant classifications using leaf images that have been used in recent years are reviewed. The review includes the image processing methods used to detect leaf and extract important leaf features for some machine learning classifiers. These machine learning classifiers are categorised according to their performance when classifying leaf images based on typical plant features, namely shape, vein, texture and a combination of multiple features. The leaf databases that are publicly available for automatic plants recognition are reviewed as well and we conclude with a discussion of prominent ongoing research and opportunities for enhancement in this area.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  • Aakif A, Khan M (2015) Automatic classification of plants based on their leaves. Biosys Eng 139:66–75. https://doi.org/10.1016/j.biosystemseng.2015.08.003

    Article  Google Scholar 

  • Altemimi A, Lakhssassi N, Baharlouei A, Watson D, Lightfoot D (2017) Phytochemicals: extraction, isolation, and identification of bioactive compounds from plant extracts. Plants 6(4):42. https://doi.org/10.3390/plants6040042

    Article  Google Scholar 

  • Aptoula E, Yanikoglu B (2013) Morphological features for leaf-based plant recognition. In IEEE international conference on image processing. IEEE, Melbourne

  • Arun C, Durairaj D (2017) Identifying medicinal plant leaves using textures and optimal colour spaces channel. Jurnal Ilmu Komputer Dan Informasi 10(1):19. https://doi.org/10.21609/jiki.v10i1.405

    Article  Google Scholar 

  • Arun C, Emmanuel W, Durairaj D (2013) Texture feature extraction for identification of medicinal plants and comparison of different classifiers. Int J Comput Appl 62(12):1–9. https://doi.org/10.5120/10129-4920

    Article  Google Scholar 

  • Bagal V, Manza R (2016) Feature extraction of plant species from leaf architecture. In: International conference on electrical, electronics, and optimization techniques (ICEEOT). IEEE, Chennai

  • Begue A, Kowlessur V, Singh U, Mahomoodally F, Pudaruth S (2017) Automatic recognition of medicinal plants using machine learning techniques. Int J Adv Comput Sci Appl 8(4):166–176. https://doi.org/10.14569/ijacsa.2017.080424

    Article  Google Scholar 

  • Bosch A, Zisserman A, Munoz X (2007) Representing shape with a spatial pyramid kernel. In: International conference on image and video retrieval. Association for Computing Machinery, New York

  • Chaki J, Parekh R, Bhattacharya S (2015) Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recognit Lett 58:61–68. https://doi.org/10.1016/j.patrec.2015.02.010

    Article  Google Scholar 

  • Charters J, Wang Z, Chi Z, Tsoi A, Feng D (2014) EAGLE: a novel descriptor for identifying plant species using leaf lamina vascular features. In: 2014 IEEE international conference on multimedia and expo workshops (ICMEW). IEEE, Chengdu

  • Chothe S, Ratnaparkhe V (2015) Plant identification using leaf images. Int J Innov Res Sci Eng Technol 4(6):660–662

    Google Scholar 

  • Cope E, Muenscher W (2001) Muenscher’s keys to woody plants. Comstock Pub. Associates, Ithaca

    Google Scholar 

  • Dahigaonkar T, Kalyane R (2018) Identification of ayurvedic medicinal plants by image processing of leaf samples. Int Res J Eng Technol 5(5):351–355

    Google Scholar 

  • Davies E (2018) Computer vision, 5th edn. Elsevier, Amsterdam

    Google Scholar 

  • Dix N (1984) Moisture content and water potential of abscissed leaves in relation to decay. Soil Biol Biochem 16(4):367–370. https://doi.org/10.1016/0038-0717(84)90034-8

    Article  Google Scholar 

  • Du J, Wang X, Zhang G (2007) Leaf shape-based plant species recognition. Appl Math Comput 185(2):883–893. https://doi.org/10.1016/j.amc.2006.07.072

    Article  Google Scholar 

  • Dudani S (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern 6(4):325–327. https://doi.org/10.1109/tsmc.1976.5408784

    Article  Google Scholar 

  • Ehsanirad A (2010) Plant classification based on leaf recognition. Int J Comput Sci Inf Secur 8(4):78–81

    Google Scholar 

  • El Mohadab M, Bouikhalene B, Safi S (2018) Predicting rank for scientific research papers using supervised learning. Appl Comput Inform. https://doi.org/10.1016/j.aci.2018.02.002

    Article  Google Scholar 

  • Fotopoulou F, Laskaris N, Economou G, Fotopoulos S (2011) Advanced leaf image retrieval via multidimensional embedding sequence similarity (MESS) method. Pattern Anal Appl 16(3):381–392. https://doi.org/10.1007/s10044-011-0254-6

    Article  MathSciNet  Google Scholar 

  • Gao L, Lin X (2018) A method for accurately segmenting images of medicinal plant leaves with complex backgrounds. Comput Electron Agric 155:426–445. https://doi.org/10.1016/j.compag.2018.10.020

    Article  Google Scholar 

  • Gardner M, Dorling S (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32(14–15):2627–2636. https://doi.org/10.1016/s1352-2310(97)00447-0

    Article  Google Scholar 

  • Grinblat G, Uzal L, Larese M, Granitto P (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424. https://doi.org/10.1016/j.compag.2016.07.003

    Article  Google Scholar 

  • Gupta S, Mazumdar S (2013) Sobel edge detection algorithm. Int J Comput Sci Manag Res 2(2):1578

    Google Scholar 

  • Güvenir H, Demiröz G, İlter N (1998) Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif Intell Med 13(3):147–165. https://doi.org/10.1016/s0933-3657(98)00028-1

    Article  Google Scholar 

  • Hastie T, Buja A, Tibshirani R (1995) Penalized discriminant analysis. Ann Statist 23(1):73–102. https://doi.org/10.1214/aos/1176324456

    Article  MathSciNet  Google Scholar 

  • Herdiyeni Y, Santoni M (2012) Combination of morphological, local binary pattern variance and color moments features for Indonesian medicinal plants identification. In: International conference on advanced computer science and information systems (ICACSIS). Depok, pp 255–259

  • Herdiyeni Y, Wahyuni N (2012) Mobile application for Indonesian medicinal plants identification using fuzzy local binary pattern and fuzzy color histogram. In: International conference on advanced computer science and information systems (ICACSIS). IEEE, Depok, pp 301–306

  • Herdiyeni Y, Nurfadhilah E, Zuhud E, Damayanti E, Arai K, Okumura H (2013) A computer aided system for tropical leaf medicinal plant identification. Int J Adv Sci Eng Inf Technol 3(1):23. https://doi.org/10.18517/ijaseit.3.1.270

    Article  Google Scholar 

  • Hothorn T, Lausen B (2005) Bundling classifiers by bagging trees. Comput Stat Data Anal 49(4):1068–1078. https://doi.org/10.1016/j.csda.2004.06.019

    Article  MathSciNet  Google Scholar 

  • Hu R, Jia W, Ling H, Huang D (2012) Multiscale distance matrix for fast plant leaf recognition. IEEE Trans Image Process 21(11):4667–4672. https://doi.org/10.1109/tip.2012.2207391

    Article  MathSciNet  Google Scholar 

  • Janani R, Gopal A (2013) Identification of selected medicinal plant leaves using image features and ANN. In: International conference on advanced electronic systems. IEEE, Pilani, pp 238–243

  • Jeon W, Rhee S (2017) Plant leaf recognition using a convolution neural network. Int J Fuzzy Logic Intell Syst 17(1):26–34. https://doi.org/10.5391/ijfis.2017.17.1.26

    Article  Google Scholar 

  • Kaburlasos V, Athanasiadis I, Mitkas P (2007) Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation. Int J Approx Reason 45(1):152–188. https://doi.org/10.1016/j.ijar.2006.08.001

    Article  Google Scholar 

  • Kalyoncu C, Toygar Ö (2015) Geometric leaf classification. Comput Vis Image Underst 133:102–109. https://doi.org/10.1016/j.cviu.2014.11.001

    Article  Google Scholar 

  • Karami N, Javid A, Haghirosadat B (2017) A review of medicinal plants effective in the treatment or apoptosis of cancer cells. Cancer Press 3(1):22. https://doi.org/10.15562/tcp.41

    Article  Google Scholar 

  • Kho S, Manickam S, Malek S, Mosleh M, Dhillon S (2017) Automated plant identification using artificial neural network and support vector machine. Front Life Sci 10(1):98–107. https://doi.org/10.1080/21553769.2017.1412361

    Article  Google Scholar 

  • Kukreja M, Johnston S, Stafford P (2012) Comparative study of classification algorithms for immunosignaturing data. BMC Bioinform 13(1):139. https://doi.org/10.1186/1471-2105-13-139

    Article  Google Scholar 

  • Kumar G, Bhatia P (2014) A detailed review of feature extraction in image processing systems. In: International conference on advanced computing & communication technologies. IEEE, Washington DC

  • Kumar N, Belhumeur P, Biswas A, Jacobs D, Kress W, Lopez I, Soares J (2012). Leafsnap: a computer vision system for automatic plant species identification. In: European conference on computer vision. SpringerLink, Florence, pp 502–516

  • Larese M, Namías R, Craviotto R, Arango M, Gallo C, Granitto P (2014) Automatic classification of legumes using leaf vein image features. Pattern Recognit 47(1):158–168. https://doi.org/10.1016/j.patcog.2013.06.012

    Article  Google Scholar 

  • Le T, Tran D, Hoang V (2014) Fully automatic leaf-based plant identification, application for Vietnamese medicinal plant search. In: Proceedings of the fifth symposium on information and communication technology. ACM Digital Library, Hanoi, pp 146–154

  • Li Y, Zhang X (2011) Improving k nearest neighbor with exemplar generalization for imbalanced classification. In: Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining. Springer, Berlin, pp 321–332

  • Lira M, de Aquino R, Ferreira A, Carvalho M, Neto O, Santos G (2007) Combining multiple artificial neural networks using random committee to decide upon electrical disturbance classification. In: International joint conference on neural networks. IEEE, Orlando

  • Liu N, Kan J (2016) Improved deep belief networks and multi-feature fusion for leaf identification. Neurocomputing 216:460–467. https://doi.org/10.1016/j.neucom.2016.08.005

    Article  Google Scholar 

  • Lulekal E, Kelbessa E, Bekele T, Yineger H (2008) An ethnobotanical study of medicinal plants in Mana Angetu District, southeastern Ethiopia. J Ethnobiol Ethnomed. https://doi.org/10.1186/1746-4269-4-10

    Article  Google Scholar 

  • Ma L, Zhao Z, Wang J (2013) ApLeafis: an android-based plant leaf identification system. In: International conference on intelligent computing. Springer, Nanning, pp 106–111

  • Mao W, Wang F (2013) New advances in intelligence and security informatics, 1st edn. Elsevier, Amsterdam

    Google Scholar 

  • Mehdipour Ghazi M, Yanikoglu B, Aptoula E (2017) Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228–235. https://doi.org/10.1016/j.neucom.2017.01.018

    Article  Google Scholar 

  • Mouine S, Yahiaoui I, Verroust-Blondet A (2013) A shape-based approach for leaf classification using multiscaletriangular representation. In: 3rd ACM conference on international conference on multimedia retrieval. ACM Digital Library, Dallas, pp 127–134

  • Munisami T, Ramsurn M, Kishnah S, Pudaruth S (2015) Plant leaf recognition using shape features and colour histogram with k-nearest neighbour classifiers. Procedia Comput Sci 58:740–747. https://doi.org/10.1016/j.procs.2015.08.095

    Article  Google Scholar 

  • Mzoughi O, Yahiaoui I, Boujemaa N, Zagrouba E (2015a) Semantic shape models for leaf species identification. In: International conference on advanced concepts for intelligent vision systems. Springer, Catania, pp 661–671

  • Mzoughi O, Yahiaoui I, Boujemaa N, Zagrouba E (2015b) Semantic-based automatic structuring of leaf images for advanced plant species identification. Multimed Tools Appl 75(3):1615–1646. https://doi.org/10.1007/s11042-015-2603-8

    Article  Google Scholar 

  • Nasir A, Rahman M, Mat N, Mamat R (2014) Automatic identification of Ficus deltoidea Jack (Moraceae) varieties based on leaf. Mod Appl Sci 8(5):121. https://doi.org/10.5539/mas.v8n5p121

    Article  Google Scholar 

  • Neto J, Meyer G, Jones D, Samal A (2006) Plant species identification using elliptic Fourier leaf shape analysis. Comput Electron Agric 50(2):121–134. https://doi.org/10.1016/j.compag.2005.09.004

    Article  Google Scholar 

  • Nguyen T, Le T, Vu H, Nguyen H, Hoang V (2017) A combination of deep learning and hand-designed feature for plant identification based on leaf and flower images. In: Asian conference on intelligent information and database systems. Springer, Kanazawa, pp 223–233

  • Noble W (2006) What is a support vector machine? Nat Biotechnol 24(12):1565–1567. https://doi.org/10.1038/nbt1206-1565

    Article  Google Scholar 

  • Panigrahi R, Borah S (2018) Rank allocation to J48 group of decision tree classifiers using binary and multiclass intrusion detection datasets. Procedia Comput Sci 132:323–332. https://doi.org/10.1016/j.procs.2018.05.186

    Article  Google Scholar 

  • Pape J, Klukas C (2014) 3-D histogram-based segmentation and leaf detection for rosette plants. In: European conference on computer vision. Springer, Zurich, pp 61–74

  • Patil V, Manza R (2015) A method of feature extraction from leaf architecture. Int J Adv Res Comput Sci Softw Eng 5(7):1025–1029

    Google Scholar 

  • Pornpanomchai C, Rimdusit S, Tanasap P, Chaiyod C (2011) Thai herb leaf image recognition system (THLIRS). Nat Sci 45:551–562

    Google Scholar 

  • Prasvita D, Herdiyeni Y (2013) MedLeaf: mobile application for medicinal plant identification based on leaf image. Int J Adv Sci Eng Inf Technol 3(2):103. https://doi.org/10.18517/ijaseit.3.2.287

    Article  Google Scholar 

  • Pushpanathan K, Hanafi M, Mashohor S, Fazlil Ilahi W (2020) Efficacy of deep learning algorithms in classifying Asian local perennial herbs in natural environment from small dataset. Manuscript in preparation

  • Rashad M, el-Desouky B, Khawasik M (2011) Plants images classification based on textural features using combined classifier. Int J Comput Sci Inf Technol 3(4):93–100. https://doi.org/10.5121/ijcsit.2011.3407

    Article  Google Scholar 

  • Rzanny M, Seeland M, Wäldchen J, Mäder P (2017) Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods 13(1):1–11. https://doi.org/10.1186/s13007-017-0245-8

    Article  Google Scholar 

  • Sainin M, Ghazali T, Alfred R (2014) Malaysian medicinal plant leaf shape identification and classification. In: Knowledge management international conference and exhibition. KMICe, Langkawi

  • Salve P, Yannawar P, Sardesai M (2018) Multimodal plant recognition through hybrid feature fusion technique using imaging and non-imaging hyper-spectral data. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.09.018

    Article  Google Scholar 

  • Sanderson M, Clough P (2019) ImageCLEF—the CLEF cross language image retrieval track|ImageCLEF/LifeCLEF—multimedia retrieval in CLEF. https://www.imageclef.org/

  • Sfar A, Boujemaa N, Geman D (2014) Confidence sets for fine-grained categorization and plant species identification. Int J Comput Vis 111(3):255–275. https://doi.org/10.1007/s11263-014-0743-3

    Article  MathSciNet  Google Scholar 

  • Shabanzade M, Zahedi M, Amin Aghvami S (2011) Combination of local descriptors and global features for leaf recognition. Signal Image Process Int J 2(3):23–31. https://doi.org/10.5121/sipij.2011.2303

    Article  Google Scholar 

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv 1409(1556)

  • Singh S, Bhamrah M (2015) Leaf identification using feature extraction and neural network. J Electron Commun Eng 10(5):134–140

    Google Scholar 

  • Singh V, Misra A (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4(1):41–49. https://doi.org/10.1016/j.inpa.2016.10.005

    Article  Google Scholar 

  • Singh A, Ganapathysubramanian B, Sarkar S, Singh A (2018) Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci 23(10):883–898. https://doi.org/10.1016/j.tplants.2018.07.004

    Article  Google Scholar 

  • Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016:1–11. https://doi.org/10.1155/2016/3289801

    Article  Google Scholar 

  • Soderkvist O (2001). Computer vision classification of leaves from Swedish Trees (Master’s Degree). Linkoping University

  • Sulc M, Matas J (2014) Texture-based leaf identification. In: European conference on computer vision. Springer, Zurich, pp 185–200

  • Sun Y, Liu Y, Wang G, Zhang H (2017) Deep learning for plant identification in natural environment. Comput Intell Neurosci 2017:1–6. https://doi.org/10.1155/2017/7361042

    Article  Google Scholar 

  • Tang Z, Su Y, Er M, Qi F, Zhang L, Zhou J (2015) A local binary pattern-based texture descriptor for classification of tea leaves. J Neurocomput 168(C):1011–1023

    Article  Google Scholar 

  • Thanikkal J, Dubey A, Thomas M (2018) Advanced plant leaf classification through image enhancement and canny edge detection. In: 7th international conference on reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, Uttar Pradesh

  • Vardhan J, Kaur K, Kumar U (2017) Plant recognition using HOG and artificial neural network. Int J Recent Innov Trends Comput Commun 5(5):746–750

    Google Scholar 

  • Vijayalakshmi B (2012) A new shape feature extraction method for leaf image retrieval. In: Fourth international conference on signal and image processing. SpringerLink, Dubai, pp 235–245

  • Vo A, Dang H, Nguyen B, Pham V (2019) Vietnamese herbal plant recognition using deep convolutional features. Int J Mach Learn Comput 9(3):363–367. https://doi.org/10.18178/ijmlc.2019.9.3.811

    Article  Google Scholar 

  • Wäldchen J, Mäder P (2017) Plant species identification using computer vision techniques: a systematic literature review. Arch Comput Methods Eng 25(2):507–543. https://doi.org/10.1007/s11831-016-9206-z

    Article  MathSciNet  Google Scholar 

  • Wäldchen J, Rzanny M, Seeland M, Mäder P (2018) Automated plant species identification—trends and future directions. PLoS Comput Biol 14(4):e1005993. https://doi.org/10.1371/journal.pcbi.1005993

    Article  Google Scholar 

  • Wang X, Liang J, Guo F (2014) Feature extraction algorithm based on dual-scale decomposition and local binary descriptors for plant leaf recognition. Digit Signal Proc 34:101–107. https://doi.org/10.1016/j.dsp.2014.08.005

    Article  Google Scholar 

  • Wang B, Brown D, Gao Y, Salle J (2015) MARCH: multiscale-arch-height description for mobile retrieval of leaf images. Inf Sci 302:132–148. https://doi.org/10.1016/j.ins.2014.07.028

    Article  Google Scholar 

  • Wang B, Gao Y, Sun C, Blumenstein M, La Salle J (2017) Can walking and measuring along chord bunches better describe leaf shapes? In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Honolulu, pp 2047–2056

  • Wang H, Tian D, Li C, Tian Y, Zhou H (2019) Plant leaf tooth feature extraction. PLoS ONE 14(2):e0204714. https://doi.org/10.1371/journal.pone.0204714

    Article  Google Scholar 

  • WHO (1999) WHO monographs on selected medicinal plants, 1st edn. World Health Organization, Geneva

    Google Scholar 

  • Wilson D, Martinez T (2000) Reduction techniques for instance-based learning algorithms. Mach Learn 38(3):257–286

    Article  Google Scholar 

  • Wu S, Bao F, Xu E, Wang Y, Chang Y, Xiang Q (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. In: IEEE international symposium on signal processing and information technology. IEEE, Giza, pp 11–16

  • Yanikoglu B, Aptoula E, Tirkaz C (2014) Automatic plant identification from photographs. Mach Vis Appl 25(6):1369–1383. https://doi.org/10.1007/s00138-014-0612-7

    Article  Google Scholar 

  • Zhao C, Chan S, Cham W, Chu L (2015) Plant identification using leaf shapes—a pattern counting approach. Pattern Recognit 48(10):3203–3215. https://doi.org/10.1016/j.patcog.2015.04.004

    Article  Google Scholar 

  • Zheng X, Wang X (2009) Feature extraction of plant leaf based on visual consistency. In: International symposium on computer network and multimedia technology. IEEE, Wuhan

  • Zhu X, Goldberg A (2009) Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn 3(1):1–130. https://doi.org/10.2200/s00196ed1v01y200906aim006

    Article  Google Scholar 

  • Zhu X, Zhu M, Ren H (2018) Method of plant leaf recognition based on improved deep convolutional neural network. Cogn Syst Res 52:223–233. https://doi.org/10.1016/j.cogsys.2018.06.008

    Article  Google Scholar 

Download references

Funding

The authors wish to acknowledge the financial supports received from the Ministry of Education, Malaysia under a Fundamental Research Grant Scheme (FRGS) (Vot Number: 5540078) and Universiti Putra Malaysia under IPS Grants (Vot Numbers: GP-IPS/2017/9529000 and GP-IPS/2017/9634300).

Author information

Authors and Affiliations

  1. Faculty of Engineering, University Putra Malaysia, 43400 UPM, Serdang, Selangor Darul Ehsan, Malaysia

    Kalananthni Pushpanathan, Marsyita Hanafi & Syamsiah Mashohor

  2. Faculty of Agriculture, University Putra Malaysia, 43400 UPM, Serdang, Selangor Darul Ehsan, Malaysia

    Wan Fazilah Fazlil Ilahi

Authors
  1. Kalananthni Pushpanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marsyita Hanafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Syamsiah Mashohor
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wan Fazilah Fazlil Ilahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marsyita Hanafi.

Ethics declarations

Conflict of interest

There is no conflict of interest to be declared.

Code availability

Not applicable.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pushpanathan, K., Hanafi, M., Mashohor, S. et al. Machine learning in medicinal plants recognition: a review. Artif Intell Rev 54, 305–327 (2021). https://doi.org/10.1007/s10462-020-09847-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-020-09847-0

Keywords

Search

Navigation