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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.

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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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  3. A. Dalvin Vinoth Kumar
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  4. P. Veeresha
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All the authors have contributed equally to this work.

Corresponding author

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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