PSO-Cascadenet: An Intelligent Hybrid Deep Learning Model for Medicinal Plant Classification

In the modern technology-oriented world, identifying medicinal plants has become very important for healthcare, biodiversity preservation, and the development of natural medicines. Traditional methods of plant identification mainly depend on expert knowledge and manual inspection, which makes the process slow and sometimes inaccurate. To address these challenges, Pso-CascadeNet presents an intelligent deep learning–based system that can recognize medicinal plants using images of their leaves. The system uses Convolutional Neural Networks (CNNs) to extract visual patterns from images, Particle Swarm Optimization (PSO) to automatically tune model parameters, and Support Vector Machines (SVM) to improve the accuracy of classification. A simple and interactive interface built with Streamlit enables users to upload leaf images and receive instant predictions, while FastAPI supports smooth backend communication and deployment. Performance evaluation using metrics such as accuracy, precision, recall, and F1-score demonstrates that the hybrid CNN–PSO–SVM model performs better than traditional classification techniques. Overall, the proposed framework offers a dependable, scalable, and user-friendly approach for digital identification of medicinal plants, benefiting research, learning, and sustainable use of herbal resources.

Herbal Plant Recognition, Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO), Support Vector Machines

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