Enhanced Medicinal Plant Classification Using Convolutional Neural Networks

Abstract

In the realm of herbal medicine, Precise recognition of medicinal plants is essential for unlocking their therapeutic benefits. To fully utilize the curative potential of medicinal plants, precise identification is essential in the domain of medicine. Traditional methods often rely on visual cues from leaves, but the deceptive similarities and variable characteristics pose challenges. To overcome these hurdles, this work introduces an innovative approach utilizing deep learning models like VGG16, RESNET50 and Inception. By focusing on the intricate features of plant leaves, our model aims to accurately distinguish between medicinal and others plants. Drawing upon the complex features of plant leaves, our model is able to identify medicinal plants with greater accuracy than existing approaches. Leveraging advanced neural network architectures, this work seeks to provide a reliable solution for medical practitioners, herbalists, and researchers, bridging traditional knowledge with modern technology to enhance the precision of medicinal plant identification.