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Segmentation and identification of medicinal plant through weighted KNN

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Abstract

Medicinal plants can provide immense contribution towards the growth of modern medicine and pharmaceutical industries to protect people from current deadly diseases like cancer and cardiovascular diseases. However, presence of thousands of plant species globally and similarity in their features like color, texture and shape makes their identification critical and immensely challenging. Moreover, utilization of traditional methods to classify plant leaf under expert’s guidance is costly, challenging and time taking process. Therefore, in this article, a Weighted KNN Classification (WKNNC) Model is adopted for the accurate identification of plant leaf images based on machine learning techniques.High quality morphological and discriminative features are obtained by using Region of Interest (ROI) images which is extracted from segmentation process. The proposed WKNNC model works upon Local Intensity Relation (LIR) and directional group encoding method to obtain high quality features. Further, the obtained feature weights provide high classification accuracy. Folio Leaf dataset is utilized to evaluate performance of proposed WKNNC model. The obtained classification accuracy is compared against several state-of-art-techniques and proposed EKNNC model outperforms all of them.

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Authors and Affiliations

  1. Department of computer Science and Engineering, Sharnbasva University, Kalburgi, India

    Savitha Patil

  2. Department of Electrical and Electronics Engineering, Godutai Engineering College for Women, Kalburgi, India

    M. Sasikala

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  1. Savitha Patil
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  2. M. Sasikala
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Patil, S., Sasikala, M. Segmentation and identification of medicinal plant through weighted KNN. Multimed Tools Appl 82, 2805–2819 (2023). https://doi.org/10.1007/s11042-022-13201-7

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  • DOI: https://doi.org/10.1007/s11042-022-13201-7

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