Blooming Insights: Flower Classification Using Data Mining Techniques

Abstract

Precise flower classification is essential in botanical research, agricultural diagnostics, and biodiversity monitoring, as accurate species identification underpins ecological sustainability and informed decision-making. This study rigorously evaluates the utilization of data mining techniques in flower classification, specifically contrasting conventional algorithms like decision trees, K-means clustering, and support vector machines with contemporary deep learning methods, particularly convolutional neural networks (CNNs) augmented by transfer learning and hybrid feature strategies. A systematic literature review examined peer-reviewed research published from 2020 to 2025. The results indicate that although conventional data mining models provide computational efficiency and interpretability, they frequently exhibit suboptimal performance on high-dimensional image datasets. CNN-based architectures consistently exhibit enhanced accuracy, robustness, and scalability, especially when integrated with data augmentation and optimization methods. Nonetheless, significant limitations persist, including constrained generalizability across varying environmental conditions, insufficient explainability, and difficulties in real-time implementation. This review enhances theoretical understanding and practical implementation by examining the progression of flower classification methodologies and suggesting future research avenues that emphasize balanced models integrating performance, transparency, and adaptability in field applications.