Classification of Indian Herbal Leaf with Random Forest Classifier

  • Trisa Das Guru Nanak Institute of Technology
  • Dola Saha Guru Nanak Institute of Technology
  • Chiranjib Dutta Guru Nanak Institute of Technology
  • Ananjan Maiti Guru Nanak Institute of Technology
Keywords: Image Processing, Shape Feature, Texture Feature, Feature Extraction, Random Forest Classifier


Herbal Leaf is normally used for preparing medicines. This includes images taken from the locality of West Bengal mainly Kolkata, with white background. By taking into account this problem of image classification, this research tries to identify herbal leaves based on the images taken on white background. Therefore, this research includes an image processing algorithm and Otsu segmentation. Then, the leaf image features are identified, based on the characteristics of the shape and texture. Herbal leaf shape and color features produce high accuracy only when both are applied at the same time. In this research, morphological features were used for shape feature extraction. The contribution of this study is using the proposed image enhancement and segmentation algorithm so that the features of the image can be extracted based on shape and color descriptors. The classification accuracy in this study has reached 97.8% with Random Forest Classifier.



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Author Biographies


Department of Computer Applications

Trisa Das, Guru Nanak Institute of Technology

Department of Computer Applications

Dola Saha, Guru Nanak Institute of Technology

Assistant Professor, Dept. of Computer Applications

Chiranjib Dutta, Guru Nanak Institute of Technology

Assistant Professor, Dept. of Computer Applications

Ananjan Maiti, Guru Nanak Institute of Technology

Assistant Professor, Dept. of Computer Science and Engineering


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How to Cite
Dasgupta, S.; Das, T.; Saha, D.; Dutta, C.; Maiti, A. Classification of Indian Herbal Leaf With Random Forest Classifier. prepare@u_foset 2022.
12th Inter-University Engineering, Science & Technology Academic Meet – 2022

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