The identification of medicinal plants is essential for traditional medicine and botanical research, but it often requires specialized knowledge and considerable time. To address these challenges, this study utilizes advanced deep learning models to automate the classification of 40 distinct medicinal plant species. The research explores the performance of Convolutional Neural Networks (CNN), MobileNet, and a hybrid model combining MobileNet with Recurrent Neural Networks (RNN). These models are trained on a comprehensive set of plant images and evaluated for their effectiveness in classification tasks, with metrics including accuracy, precision, and recall. The CNN serves as a strong baseline for image classification, while MobileNet is employed for its computational efficiency, making it suitable for environments with limited resources. The hybrid MobileNet and RNN model is assessed for its potential to capture sequential and contextual patterns within the image data. The results of this study provide insights into the development of more efficient and accessible automated plant identification systems with practical applications for researchers, herbalists, and other practitioners. These advancements have the potential to enhance the speed, accuracy, and reliability of medicinal plant classification, supporting the growth of traditional medicine and botanical research
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