A Hybrid CNN-Transformer Architecture with Bidirectional Cross-Attention Fusion for Efficient Flower Recognition - Hersh HAMA; Shayan Jalal; Saman Omer; Mohammed Ahmed (AI Society & STMIK Indonesia, 2026)
Automated flower recognition is crucial to the fields of agriculture and biodiversity monitoring, but deep learning models are extremely high in computing demand for resource-constrained devices. In this paper, a compact and efficient hybrid model based on EfficientNet-B4 and ViT-Small/16 is introduced. The design uses a bidirectional cross-attention fusion mechanism that uses both local edge-level features and global context to learn highly discriminative representations for fine-grained classification. The model was tested using 3,500 images from 35 species of flowers from a curated, augmented database, using 5-fold cross-validation. It outperformed the state-of-the-art architectures such as Swin Transformer and ResNet50, with 98.71% accuracy and a 98.80% F1 score with high statistical stability (95% CI:98.34%–99.09%) and with faster convergence rate. Statistically, these improvements were confirmed by pair-wise and Wilcoxon signed-rank statistical tests, which indicate the model's potential in resource-efficient real-world automated plant identification.