Abstract: Medical identification of plants serves important functions for healthcare systems as well as pharmaceutical development and protection of biodiversity. The identification process which depends on manual techniques demands extensive time from experts so automated solutions prove to be crucial. This paper investigates deep learning techniques for medicinal plant classification through combination of ResNet and EfficientNet structures. The training of our model utilized a large database consisting of medicinal plants which incorporated EfficientNet and ResNet architectures to extract complex leaf patterns together with their textual features and color schemes in the leaves.
Users can access Plantify system through its easy-to-use web interface which provides functionality for botanists and researchers along with healthcare professionals to submit plant images for instant classification. A collection of medicinal plant pictures served as input for model training and evaluation through which their main visual characteristics including leaf styles along with textures and color patterns were analysed. The experimental outcomes prove that EfficientNet surpasses traditional models both in accuracy performance and computational efficiency requirements which makes it appropriate for mobile application usage.
Keywords: Convolutional Neural Networks, EfficientNet, ResNet, Medicinal Plants, Image Processing,Machine Learning.
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DOI:
10.17148/IJARCCE.2025.14265
