A Comprehensive Review of Flower Classification Techniques Using Deep Learning

This paper analyzes deep learning techniques for categorizing flowers, covering new advances, issues, and advancements in this emerging field. As the significance of automated flower identification continues to grow across many domains, deep learning algorithms have emerged as powerful tools for attaining accurate and effective categorization. By combining a variety of papers, methods, and datasets, this study offers a structured overview of the state-of-the-art in deep learning-based flower categorization. The main concepts, architectures, pre-processing techniques, transfer learning methodology, and performance evaluations employed in diverse research are covered in the article. By examining the benefits, drawbacks, and contrasting assessments of different methods, this research provides helpful insights for academics, practitioners, and enthusiasts interested in employing deep learning for flower categorization.