Year: 2025
Type: article
Abstract: —The accurate identification of medicinal plants is crucial for ethnobotanical research, pharmaceutical development, and biodiversity conservation. Traditional identification methods rely on expert knowledge, which is time-consuming and not always accessible. This paper proposes a deep learning-based framework using Convolutional Neural Networks (CNNs) for the automated classification of Indian medicinal plants through leaf imagery. We systematically compare the performance of five state-of-the-art CNN architectures: MobileNetV2, ResNet50, InceptionV3, Xception, and DenseNet121. InceptionV3 achieves the best classification accuracy of 95%, demonstrating robust performance in extracting spatial hierarchies of leaf features. Furthermore, we introduce Tinkerit, a lightweight desktop appli- cation with a graphical user interface (GUI), enabling real-time plant recognition. Our solution paves the way for modernizing herbal medicine practices through AI-driven tools, contributing to both healthcare innovation and ecological preservation.
DOI: 10.55041/ijsrem47867
Authors: author, Dr. Keerthi Kumar HM
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410410214