Implementation of VGG6 for Image Processing of Aromatic Plants Using Python

Abstract

Big Data Analytics has gained significant popularity in recent years, with many companies integrating it into their information technology roadmaps to enhance business performance. However, surveys indicate that Big Data Analytics demands substantial resources, including technology, costs, and talent, which often leads to failures in the initial stages of implementation. This study proposes a VGG6 architecture approach, intended to provide a framework for the initial implementation of Big Data Analytics. The study's outcomes include the implementation of the VGG6 architecture for processing images of aromatic plants using Python. Furthermore, this approach enabled the development of a Minimum Viable Product (MVP) solution that adheres to general Big Data principles, such as the 3Vs (Volume, Velocity, and Variety), and encompasses key technological components: 1) Data Storage and Analysis, 2) Knowledge Discovery and Computational Complexity, 3) Scalability and Data Visualization, and 4) Information Security.