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CNN-based Indian medicinal leaf type identification and medical use recommendation

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Abstract

Medicinal leaves are playing a vital role in our everyday life. There are an enormous amount of species present in the world. Identification of each type would be a tedious task. Using image processing technology, we can overcome this problem by providing computer vision with the help of a convolution neural network (CNN). The objective of this research is to find out the best CNN model that helps in classifying the plant leaf species and identifying its category. In this research work, the proposed basic CNN model consisting of four convolution layers uses ten different medicinal leaf species each belonging to two categories providing an accuracy of \(96.88\%\).

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Data availability statement

The data that support the findings of this study are available from the first author upon reasonable request.

References

  1. Mukherjee G, Tudu B, Chatterjee A (2021) A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: case studies with Neem, Tulsi and Kalmegh leaves. Soft Comput 25:14119–14138. https://doi.org/10.1007/s00500-021-06139-9

    Article  Google Scholar 

  2. 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. https://doi.org/10.1155/2016/3289801

    Article  PubMed  PubMed Central  Google Scholar 

  3. Xin M, Wang YX (2019) Research on image classification model based on deep convolution neural network. EURASIP J Image Video Process. https://doi.org/10.1186/s13640-019-0417-8

    Article  Google Scholar 

  4. Kschandra R, Srinivas Y, Krishna M (2020) Study on image processing using deep learning techniques. Mater Today Proc. https://doi.org/10.1016/j.matpr.2020.09.536

    Article  Google Scholar 

  5. Azadnia R, Al-Amidi MM, Mohammadi H, Cifci MA, Daryab A, Cavallo E (2022) An AI based approach for medicinal plant identification using deep CNN based on global average pooling. Agronomy. https://doi.org/10.3390/agronomy12112723

    Article  Google Scholar 

  6. Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE international symposium on signal processing and information technology, pp 11–16. https://doi.org/10.1109/ISSPIT.2007.4458016.

  7. Anami BS, Nandyal SS, Govardhan A (2010) A combined color, texture and edge features based approach for identification and classification of Indian medicinal plants. Int J Comput Appl 6(12):45–51

    Google Scholar 

  8. Gopal A, Prudhveeswar RS, Gayatri V (2012) Classification of selected medicinal plants leaf using image processing. In: International conference on machine vision and image processing. https://doi.org/10.1109/MVIP.2012.6428747

  9. Kan HX, Jin L, Zhou FL (2017) Classification of medicinal plant leaf image based on multi-feature extraction. Pattern Recognit Image Anal 27:581–587. https://doi.org/10.1134/S105466181703018X

    Article  Google Scholar 

  10. Vo AH, Dang HT, Nguyen BT, Pham VH (2019) Vietnamese herbal plant recognition using deep convolutional features. Int J Comput Appl 9(3):363–367

    Google Scholar 

  11. Dudi B, Rajesh V (2019) Medicinal plant recognition based on CNN and machine learning. Int J Adv Trends Comput Sci Eng 8(4):999–1003

    Article  Google Scholar 

  12. Zin IA, Ibrahim Z, Isa D, Aliman S, Sabri N, Mangshor NN (2020) Herbal plant recognition using deep convolutional neural network. Bull Electr Eng Informat 9(5):2198–2205

    Article  Google Scholar 

  13. Anchitaalagammai JV, Shantha Lakshmi Revathy JS, Kavitha S, Murali S (2021) Factors influencing the use of deep learning for medicinal plants recognition. J Phys 2089(1):012055. https://doi.org/10.1088/1742-6596/2089/1/012055

    Article  Google Scholar 

  14. Malarvizhi K, Sowmithra M, Gokula Priya D, Kabila B (2021) Machine learning for plant species lassification using leaf vein morphometric. Int J Eng Res Technol https://doi.org/10.17577/IJERTV10IS040273

  15. Azadnia R, Al-Amidi MM, Mohammadi H, Cifci MA, Daryab A, Cavallo E (2022) An AI based approach for medicinal plant identification using deep CNN based on global average pooling. Agronomy 12(11):2723. https://doi.org/10.3390/agronomy12112723

    Article  Google Scholar 

  16. Islam M, Ria NJ, Ani JF, Masum AKM, Abujar S, Hossain SA (2022) Deep learning based classification system for recognizing local spinach. In: Advances in deep learning, artificial intelligence and robotics (ICDLAIR), https://doi.org/10.1007/978-3-030-85365-5-1

  17. Manikanta Sanjay V, Kyalkond SA, Aithal SS, Kumar PS (2022) A novel approach to classification of ayurvedic medicinal plants using neural networks. Int J Eng Res Technol https://doi.org/10.17577/IJERTV11IS010128

  18. Suresh V, Krishnan M, Hemavarthini M, Jayanthan K, Gopinath D (2020) Plant disease detection using image processing. Int J Eng Res Technol https://doi.org/10.17577/IJERTV9IS030114

  19. Viknesh GS, Arun Yagan (2022) Leaf classification for plant recognition using EfficientNet architecture, Int J Eng Res Technol 10(11):1–5. https://doi.org/10.17577/IJERTV10IS110021

  20. Pathirana CK, Madhujith T, Eeswara J (2020) Bael (Aegle marmelos L. Corrêa), a medicinal tree with immense economic potentials. Adv Agric https://doi.org/10.1155/2020/8814018

  21. Rai KK, Trivedi RV, Umekar MJ (2019) Review on betel leaf used in various ailments. Int J Pharmacognosy, 6(8):259–267. https://doi.org/10.13040/IJPSR.0975-8232.IJP.6(8).259-67

  22. Rani YS, Reddy VJ, Basha SJ, Koshma M, Hanumanthu G, Swaroopa P (2017) A review on Solanum nigrum. World J Pharmacy Pharmaceut Sci, 6:293–303. https://doi.org/10.20959/wjpps201712-10538

  23. Akbaribazm M, Khazaei F, Naseri L, Pazhouhi M, Zamanian M, Khazaei M (2021) Pharmacological and therapeutic properties of the Red Clover (Trifolium pratense L.): an overview of the new findings. J Tradit Chinese Med, 41(4): 642. https://doi.org/10.19852/j.cnki.jtcm.20210604.001

  24. Balakrishnan R, Vijayraja D, Jo SH, Ganesan P, Su-Kim I, Choi DK (2020) Medicinal profile, phytochemistry, and pharmacological activities of Murraya Koenigii and its primary bioactive compounds. Antioxidants 9(2):101. https://doi.org/10.3390/antiox9020101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wani SA, Kumar P (2018) Fenugreek: a review on its nutraceutical properties and utilization in various food products. J Saudi Soc Agric Sci 17(2):97–106. https://doi.org/10.1016/j.jssas.2016.01.007

    Article  Google Scholar 

  26. Kalita S, Kumar G, Karthik L, Rao KVB (2012) A review on medicinal properties of Lantana camara Linn. Res J Pharmacy Technol 5(6):711–715

    Google Scholar 

  27. Nayak P, Kumar T, Gupta AK, Joshi NU (2020), Peppermint a medicinal herb and treasure of health: a review. J Pharmacognosy Phytochem, 9(3), 1519–1528. https://doi.org/10.22271/phyto.2020.v9.i3y.11525

  28. Subapriya R, Nagini S (2005) Medicinal properties of neem leaves: a review. Curr Med Chem Anti-Cancer Agents 5(2):149–156. https://doi.org/10.2174/1568011053174828

    Article  CAS  PubMed  Google Scholar 

  29. Prakash PA, Gupta N (2005) Therapeutic uses of Ocimum sanctum Linn (Tulsi) with a note on eugenol and its pharmacological actions: a short review. Indian J Physiol Pharmacol 49(2):125

    CAS  PubMed  Google Scholar 

  30. Wang Y, Li Y, Song Y, Rong X (2020) The influence of the activation function in a convolution neural network model of facial expression recognition. Appl Sci 10(5):1897. https://doi.org/10.3390/app10051897

    Article  CAS  Google Scholar 

  31. Hao W, Yizhou W, Yaqin L, Zhili S (2020) The role of activation function in CNN. In: International conference on information technology and computer application, pp 429–432. https://doi.org/10.1109/ITCA52113.2020.00096

  32. Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. https://doi.org/10.48550/arXiv.1811.03378

  33. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629. https://doi.org/10.1007/s13244-018-0639-9

  34. Basha SHS, Dubey SR, Pulabaigari V, Mukherjee S (2019) Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing 378:112–119. https://doi.org/10.1016/j.neucom.2019.10.008

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India

    S. Praveena, S. M. Pavithra & P. Veeresha

  2. Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, 560029, India

    A. Dalvin Vinoth Kumar

Authors
  1. S. Praveena
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  2. S. M. Pavithra
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  3. A. Dalvin Vinoth Kumar
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  4. P. Veeresha
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Contributions

All the authors have contributed equally to this work.

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Correspondence to P. Veeresha.

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Appendices

Appendices

Importing all the necessary packages:

figure a

Analyzing the original dimensions of the input image:

figure b
figure c

Convert image to n-dimensional array, this is how the model reads the image:

figure d

Reducing the dimensions of the image to the target size \(227\times 227 \times 3\)

figure e
figure f

Used to rescale pixels values range from 0 to 255:

figure g

Importing training data from the directory (\(80\%\) of the data):

figure h

Importing validating data from the directory (\(10\%\) of the data):

figure i

Printing the indices for the data:

figure j

Model construction using sequential layers of convolution,maxpooling and fully connected:

figure k

Definig the learning rate, metrics:

figure l

Training the model for 10 epochs:

figure m

Plotting training accuracy and validation accuracy:

figure n
figure o

Plotting training loss and validation loss:

figure p
figure q

Importing test data from the directory (\(10\%\) of the remaining data not seen by the model):

figure r

Predicting the test dataset:

figure s

Softmax layer output array can be obtained using:

figure t

The above array is then processed to identify the class label to which the leaf belongs to:

figure u

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Praveena, S., Pavithra, S.M., Kumar, A.D.V. et al. CNN-based Indian medicinal leaf type identification and medical use recommendation. Neural Comput & Applic 36, 5399–5412 (2024). https://doi.org/10.1007/s00521-023-09352-9

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