Automated Medicinal Plant Identification Using Deep Convolutional Neural Networks

The accurate determining different species of medicinal plants presents a significant challenge in ethnobotany and healthcare applications due to morphological similarities and environmental variations. This research work develops an automated system leveraging CNN architectures for medicinal plant recognition using leaf imagery. The system integrates preprocessing, feature extraction, and multi-class classification to enable real-time plant detection. Image preprocessing techniques such as resizing, normalization, and augmentation enhance model robustness against lighting and background variations. The CNN automatically extracts discriminative morphological features, including leaf venation, margin patterns, and textural properties, through hierarchical learning. Transfer learning with progressive fine-tuning strategies is employed to improve feature generalization and classification accuracy. Experimental evaluation demonstrates that the model achieves an accuracy level of approximately 93%, effectively distinguishing visually similar plant species. The trained model is deployed in a Flask-based interactive web interface through which users can upload leaf images for real-time identification. Along with the prediction, the system displays the plant’s scientific name, medicinal properties, and therapeutic benefits, providing an accessible and intelligent platform for automated medicinal plant recognition.