International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 4 | Year 2025 | Article Id. IJCTT-V73I4P123 | DOI : https://doi.org/10.14445/22312803/IJCTT-V73I4P123

Identification of Medicinal Plants Using Machine Learning Algorithms

P. Sandeep Kumar, M. Rajeshwar, Giridhar Paida, Kodi Satheesh

Revised Accepted Published
19 Apr 2025 24 Apr 2025 30 Apr 2025

Citation :

P. Sandeep Kumar, M. Rajeshwar, Giridhar Paida, Kodi Satheesh, "Identification of Medicinal Plants Using Machine Learning Algorithms," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 4, pp. 167-172, 2025. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V73I4P123

Abstract

Medicinal plants have long been a cornerstone of the pharmaceutical and healthcare industries due to their natural healing properties. However, conventional plant identification techniques are laborious and ineffective because they depend on specialized knowledge and are prone to mistakes. Automation using computer vision and Artificial Intelligence (AI) has become necessary as a result of this difficulty. Researchers have long struggled to identify and categorize plant species using subtle visual characteristics. By utilizing a Convolutional Neural Network (CNN) with the VGG16 architecture, this study seeks to automate the Identification of medicinal plants through machine learning. The Plants Type Dataset, which includes 30,000 plant photos divided into 30 plant classes spanning seven plant types—crops, fruits, industrial, medicinal, nuts, tubers, and vegetables—was utilized in this project. There are 1,000 photos in each class featuring common plants like bananas, coconuts, and pineapples and less common ones like galangal and bilimbi. The VGG16 model is the study's most successful machine learning algorithm because it can automatically extract visual features from the plant images, significantly improving classification performance. The findings show that deep learning methods—specifically, VGG16—can provide quicker and more precise plant identification. This strategy may transform drug development and botanical research while opening new avenues for discovering therapeutic plants. The results highlight how machine learning has the potential to revolutionize plant identification procedures, advancing scientific understanding and the use of medicinal plants.

Keywords

Convolutional Neural Networks (CNNs), Machine Learning, Plants Type Dataset, Support Vector Machines (SVMs),VGG16.

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