Flower classification is a challenging task in computer vision, requiring models to discern subtle visual differences among a vast array of floral species. In this project, we propose a novel approach to flower classification leveraging the Vision Transformer (ViT) algorithm, a cutting-edge deep learning architecture that has demonstrated remarkable success in image recognition tasks. The ViT model replaces traditional convolutional layers with self-attention mechanisms, allowing it to capture long-range dependencies and global context in images more effectively.Our methodology involves pre-processing a comprehensive dataset of floral images, extracting features using ViT, and training a robust classification model. The dataset encompasses a diverse range of flowers, capturing variations in color, shape, and size. The ViT model's attention mechanisms enable it to learn hierarchical representations, improving its ability to differentiate between subtle visual nuances characteristic of different flower species.We conduct extensive experiments to evaluate the proposed approach's performance, comparing it with traditional convolutional neural networks (CNNs) commonly used in flower classification tasks. Additionally, we explore techniques for model interpretability, shedding light on the decision-making process of the ViT-based classifier.The results demonstrate the effectiveness of the Vision Transformer in flower classification, surpassing the performance of conventional CNNs. The ViT model exhibits enhanced generalization capabilities and robustness to variations in illumination and background. Furthermore, our interpretability analysis provides insights into the discriminative features learned by the ViT model, contributing to a better understanding of its decision-making process.This study not only advances the realm of computer vision but also provides valuable insights applicable in agriculture, horticulture, and ecological monitoring. The constructed model highlights the capabilities of Deep Learning in tackling intricate classification challenges, laying groundwork for forthcoming endeavors in automating plant species identification and conservation initiatives.
