Medicinal Plant Identification and Classification

Abstract: Medicinal plants play a significant role in healthcare systems due to their therapeutic and healing properties. Accurate identification of medicinal plants is essential for their proper utilization in traditional and modern medicine. However, manual identification requires expert botanical knowledge and is often time-consuming and error-prone because many plant species possess visually similar leaf structures. This project proposes an intelligent Medicinal Plant Identification and Classification system using deep learning techniques to automate the plant recognition process.

The proposed system utilizes Convolutional Neural Networks (CNNs) to analyze leaf images and classify medicinal plants based on visual features such as shape, texture, vein patterns, and color characteristics. Image preprocessing techniques including resizing, normalization, and augmentation are applied to improve model performance and handle variations in lighting, orientation, and background conditions. The system integrates Firebase Firestore for storing medicinal plant information such as scientific names, medicinal uses, and safety considerations.

Experimental evaluation demonstrates that the CNN-based approach provides high classification accuracy and reliable prediction results compared to conventional machine learning techniques. The proposed system reduces manual effort, improves identification speed, and provides an efficient and user-friendly solution for students, researchers, farmers, and healthcare enthusiasts. Overall, the project highlights the effective application of artificial intelligence and deep learning in botanical research and medicinal plant preservation.

Keywords: Medicinal Plants, Deep Learning, Convolutional Neural Network, Image Classification, Plant Identification, Artificial Intelligence, Firebase Firestore.