AI-based automated weight prediction in cattle for herd health surveillance

Early and accurate monitoring of livestock health is critical for effective disease prevention, welfare assurance, and sustainable farm management. Labor-intensive and stressful livestock weighing methods remain a major bottleneck for effective herd health surveillance in large-scale operations. This study presents a data-driven Walk-Over Weighing System (WoWS) enhanced with Fast Fourier Transform (FFT) and machine learning (ML) algorithms to provide a non-invasive, automated solution for real-time weight estimation in cattle. Dynamic weight signals from 86 dairy cows were collected twice daily during routine milking using a walk-over-weighing (WoWS) platform at the Burdur MAKU farm. Raw force-time signals were pre-processed and transformed using FFT to reduce noise and extract spectral-domain features relevant for weight estimation. Six ML models, including Support Vector Regression (SVR), were evaluated for prediction performance. The SVR model yielded the highest accuracy (MAE: 2.3 kg, R²: 0.999). The system's functionality was further extended through integration with Internet of Things (IoT) frameworks for real-time data collection and anomaly detection. Heatmaps and time-aligned weight distributions validated the system's robustness under dynamic field conditions. This FFT- and AI-enhanced WoWS offers a scalable and effective tool for herd-level health surveillance by enabling continuous monitoring, early detection of abnormal weight trends (e.g., weight loss due to disease onset or inadequate feeding), and remote decision-making. The proposed system supports One Health principles by reducing manual handling, minimizing animal stress, improving welfare, and lowering labor demands, thereby contributing to more sustainable and efficient livestock-farming practices. Future directions include expanding multi-sensor integration and epidemiological modeling for more comprehensive livestock health management.