Research on an automatic flower variety recognition system based on deep learning

Image recognition algorithms centered on deep learning demonstrate highly efficient feature learning and classification capabilities in complex natural environments. Addressing the limitations of traditional flower recognition methods— which are significantly affected by variations in lighting, angle, and background—this study constructs an end-to-end automated flower variety identification system. The system employs convolutional neural networks for multi-layer feature extraction and incorporates attention mechanisms to enhance responses in key regions. Model training and lightweight deployment are accomplished within a GPU parallel computing environment. Experimental results demonstrate that the developed system achieves high accuracy and stability in multi-category sample recognition, enabling efficient flower identification in complex scenarios.