Medicinal Plant Leaf Dataset with name table(mostly found in Paschim Maharashtra.)

Published: 29 April 2024| Version 1 | DOI: 10.17632/xzy9mh2z65.1
Sheetal Patil


Digitizing a medicinal plant leaf dataset enhances its utility, accessibility, and potential for research and innovation in the fields of botany, pharmacology, and natural medicine. Digital datasets enable advanced data analysis techniques, such as machine learning algorithms, statistical analysis, and data mining. Researchers can uncover patterns, correlations, and trends within the dataset, leading to new insights and discoveries. With advancements in technology and analytical techniques, future generations can leverage this dataset to identify potential drug candidates from natural sources. By studying the chemical composition and biological activity of medicinal leaves, they can develop new pharmaceuticals with improved efficacy and fewer side effects. dataset consists of 45 classes of plant species found in Paschim Maharashtra, totaling around 8000 images. These images were captured using a Redmi K50 with a 64 MP camera. The dataset was likely compiled through a combination of methods, including manual collection and web scraping. Each plant species is associated with its common name and medicinal significance. This dataset serves as a valuable resource for researchers and enthusiasts interested in studying the medicinal properties of various plant species native to the region. Accurate classification of medicinal leaves helps in identifying plants with therapeutic properties. This is crucial for Ayush practitioners who rely on specific plants for preparing herbal medicines and remedies. The dataset can be used to train machine learning models for image classification tasks. By feeding the model with labeled images of medicinal plants, it can learn to classify new images into one of the predefined classes. This can aid in automated identification of plant species, which is useful for botanists, pharmacologists, and herbalists.



Bharati Vidyapeeth Deemed University College of Engineering Pune


Computer Vision Algorithms