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Medicinal plants have been an essential source of remedies and treatments for various ailments throughout human history. This paper presents an innovative approach that combines machine learning and image processing to identify various medicinal plants and extract information about their traditional and modern uses. This method offers efficiency and dependability by automating the identification procedure, which lessens the reliance on human expertise and may hasten the discovery and application of medicinal plants for therapeutic purposes. In this work identification of the medical plats is done thought the shape of the plant leave, text features using digital image processing techniques, color using Convolutional Neural Works (CNN). We have achieved an accuracy of 98.6% in identifying the medical plants and we have compared our work with the existing literature with various parameters like precision, accuracy and f1 score.

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Identification of Different Medicinal Plants Using Machine Learning and Image Processing

  • 会议论文

  • 发表日期：

    2025年

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  • Kiran Sree Pokkuluri,
  • Ch Phaneendra Varma,
  • Ramesh Babu Gurujukota,
  • P. B. V. Raja Rao,
  • S. S. S. N. Usha Devi N,
  • M. Prasad,
  • Nagaraju Pamarthi,
  • P. J. R. Shalem Raju

摘要

Medicinal plants have been an essential source of remedies and treatments for various ailments throughout human history. This paper presents an innovative approach that combines machine learning and image processing to identify various medicinal plants and extract information about their traditional and modern uses. This method offers efficiency and dependability by automating the identification procedure, which lessens the reliance on human expertise and may hasten the discovery and application of medicinal plants for therapeutic purposes. In this work identification of the medical plats is done thought the shape of the plant leave, text features using digital image processing techniques, color using Convolutional Neural Works (CNN). We have achieved an accuracy of 98.6% in identifying the medical plants and we have compared our work with the existing literature with various parameters like precision, accuracy and f1 score.