Resource-constrained hybrid attention-driven approach for enhanced interpretability and scalability in multi-event livestock condition classification and monitoring

• Proposes CATT-Net framework by integrating PCA, LSTM and Transformer encoders. • Achieves high accuracy (99.74 %) across four diverse datasets excelling SOTA studies. • Optimizes for real-time use with 1.72 MB model, 144.15k parameters and 3.77 MFLOPs. • Incorporates SHAP-based explainability, enhancing model interpretability. • Validated on real agrotech data in Bangladesh with extensive ablation experiments. Precision Livestock Farming (PLF) enhances dairy farming by integrating artificial intelligence (AI) and sensor technologies for health, productivity, and welfare monitoring. However, existing methods remain resource-intensive, difficult to scale, and limited in handling diverse livestock events. To address these challenges, we propose CATT-Net, a lightweight and interpretable hybrid deep learning framework for multi-event livestock condition classification, covering health-related, reproductive, and stress-induced behavioral events. It integrates Principal Component Analysis (PCA) for dimensionality reduction, Squeeze-and-Excitation (SE) blocks for feature recalibration, LSTMs for temporal dynamics, Transformer encoders for global representation, and an Additive Attention mechanism for focused decision-making. Evaluated on four diverse datasets from experimental and commercial farms, CATT-Net achieves high accuracy (99.74 %, 96.10 %, 98.81 %, and 95.85 %) while maintaining efficiency (1.72 MB, 144k parameters, 3.77 MFLOPs), enabling deployment in resource-limited settings. SHAP-based analysis ensures transparency, and ablation studies confirm the contribution of each component. Compared to state-of-the-art transformer models and simpler baselines, CATT-Net achieves higher accuracy while requiring substantially less computational cost. External validation using real-world data from Adorsho PraniSheba, Bangladesh, further demonstrates its practical applicability for livestock monitoring. Code availability: https://github.com/fariya17/CATT-Net