Abstract: In order to provide information about medicinal plants, this research presents a unique approach that combines handwriting recognition and image categorization with a chatbot. The project utilizes the Inceptionv3 algorithm for image classification to identify leaves and the medicines derived from them. For handwritten recognition, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are employed to identify medicine names. An Al chatbot is then used to resolve user queries about these medicines and provide additional details such as home remedies. The project addresses the problem of efficiently retrieving information about medicinal plants and their uses, which is challenging due to the vast amount of information. available and the need for accurate identification. Existing approaches often lack the integration of multiple technologies or focus on a specific aspect of the problem. This work contributes by providing a comprehensive solution that combines image classification, handwritten recognition, and chatbot technology. The significance of this work lies in its potential to improve access to information about medicinal plants, which can have a significant impact on healthcare and the environment. The project was conducted over a period of November-February at Guntur and has the potential to benefit a wide range of stakeholders, including healthcare professionals, researchers, and individuals interested in natural remedies.
Keywords: Medicinal plants, chatbot, image classification, handwritten recognition, CNN, herbal medicine, natural remedies, personalized recommendations.
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DOI:
10.17148/IJARCCE.2025.14414
