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Abstract

Pharmaceutical field, especially to ensure the safety, effectiveness, and consistency of plant-based products. In the digital era, digitizing herbal identification is increasingly relevant to improve accessibility and precision, while bridging traditional knowledge with modern pharmaceutical standards. This research aims to develop a Digital pharmacopeia system that includes herbal database features, computer vision technology for plant identification, text search based on a full-text search algorithm, and API integration to support connectivity between components. The system development process uses an image classification technology-based approach that utilizes the Convolutional Neural Network (CNN) algorithm to ensure a high level of accuracy. The results show that the system has successfully improved efficiency and accuracy in herbal plant recognition, with the identification accuracy rate reaching 96% in benchmark testing. In addition, the system also offers a modern solution to support pharmaceutical research, education, and practice. By utilizing digital technology, the system is expected to become a reliable tool for pharmaceutical professionals, researchers, and the general public, while encouraging the preservation and sustainable use of biodiversity. The successful development of this system provides a strong foundation for further innovations in pharmacy and botany‎.

Keywords: Digital pharmacopeia, Herbal plants, Plant identification, Computer vision, Pharmaceutical technology

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