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All Journal The Indonesian Journal of Computer Science
Saman Omer
Department of Software Engineering, College of Engineering University of Raparin, Ranya, Iraq
Computer Science & IT Electrical & Electronics Engineering Engineering

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A Hybrid CNN-Transformer Architecture with Bidirectional Cross-Attention Fusion for Efficient Flower Recognition Hersh HAMA; Shayan Jalal; Saman Omer; Mohammed Ahmed
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i3.5181

Abstract

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.
Co-Authors Hersh Hama Mohammed Ahmed Shayan Jalal