The identification and classification of medicinal plants play a crucial role in traditional medicine, especially in developing countries where access to modern healthcare is limited. However, traditional methods for plant identification are time-consuming and often prone to errors. The rapid development of deep learning (DL) techniques has presented an effective solution to these challenges. This project investigates the use of various deep learning models, including VGG19, MobileNetV2, and a hybrid model combining VGG16 and Long Short-Term Memory (LSTM) networks, for the identification and classification of medicinal plants. The proposed models are trained on the Indian Medicinal Plant Image Dataset, which consists of high-quality images of medicinal plants. By leveraging deep learning algorithms, the system aims to automate the process of plant classification with high accuracy and speed, providing a reliable and scalable solution for medicinal plant identification. The results demonstrate that deep learning techniques can significantly enhance the accuracy and efficiency of plant classification, offering great potential for use in healthcare, agriculture, and conservation.

Keywords: Medicinal Plants Identification, Deep Learning (DL), VGG19, MobileNetV2, Hybrid Model (VGG16 + LSTM), Plant Classification, Image Classification, LSTM Networks, Indian Medicinal Plant Dataset, Traditional Medicine, Machine Learning, Computer Vision, Automation in Healthcare.