Medicinal Plant Classification using Machine Learning


  • From Vedic times plants have been used as a source of medicine in ayurveda.
  •  In the preparation of ayurvedic medicine, identification of correct plant is the most important step, which have been done manually. Due to demand of mass production, Identification of these plants automatically is important.
  •  In this project we have been implement a technique for medicinal plant identification using random forest algorithm, an ensemble supervise machine learning algorithm based on color ,texture and geometrical features
  •  Then reduced feature vectors are inputted into the classification model. Convolutional Neural Network (CNN) is used for classification and identifying the animal class.


  • Several studies have been conducted in order to develop tools for the identification of plants during the last 10 years.
  • One of the most authoritative works in the field of plant classification has been done by Wu et al.
  • From five basic geometric features, and then Principle Component Analysis (PCA)) is used for dimension reduction so that fewer inputs could be sent to a probabilistic neural network (PNN).
  • They achieved an average accuracy of 90.3% with the Flavia dataset.


  • Low Accuracy Achieved
  • Less number of plants identified in Database which is small database in real-time.
  • Low Light Plant images will not be predicted correctly.


  • The image processing techniques have been established to optimize the footprints, input image is converted into greyscale, Edge detection on the image.
  • All the images in the dataset are read, processed, and feature extracted, raw data is loaded for classification of input image.
  • This project proposed a Fuzzy C-means response, for extracting texture features and preserves texture features of an image in frequencies.
  • Selective scale and orientation filter is applied on input image to acquire texture features.
  • And Segmentation requires separating the image from the background for efficient classification.
  • Next step is extracting templates of the Leaves.
  • In template matching process along with template updating is specified.
  • CNN classifier is used for identifying the medicinal plant.
  • Since, our proposed method falls on the classification of multiple classes, the binary CNN model has been extended to multinomial logistic regression.
  • Through a combination of binary CNN, multiple groups are compared by the multinomial logit .


Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements:

  • Hard Disk: Greater than 500 GB
  • RAM: Greater than 4 GB
  • Processor: I3 and Above

 Including Packages

* Base Paper

* Complete Source Code

* Complete Documentation

* Complete Presentation Slides

* Flow Diagram

* Database File

* Screenshots

* Execution Procedure

* Readme File

* Addons

* Video Tutorials

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