Integrating deep learning and mobile imaging for assessment of automated conformational indices and weight prediction in Brahman cattle

Abstract

Accurate, non-invasive assessment of cattle body conformation and weight is critical for advancing productivity and genetic improvement in tropical beef production systems. Conventional methods are labor-intensive, require large equipment, involve direct animal contact, cause stress to animals, and are often impractical for smallholders under resource-limited conditions and lacking proper infrastructure. This study presents a novel, smartphone-based system for real-time body measurement and weight estimation in Brahman cattle using a cloud-integrated artificial intelligence (AI) model. A total of 12,660 side-view images were collected and annotated for hip depth (HD) and body length (BL), with YOLOv11 convolutional neural network variants trained and validated. The YOLOv11m model demonstrated the best performance (precision: 99.85%, recall: 100%, F1-score: 99.92%, IoU: 97.68 ± 1.31%), with automated measurements showing strong agreement with manual ImageJ data (MAPE < 0.4%). HD and BL were highly correlated in both sexes (r = 0.98–0.99) and moderately predictive of body weight (r = 0.57–0.59). A multiple regression model using HD and BL achieved the highest prediction accuracy for body weight (MAE = 43.44 kg; MAPE = 8.91%). The system was deployed through a LINE messaging chatbot app, enabling users to submit cattle images and receive instant measurements and weight estimations directly via smartphone–eliminating the need for specialized hardware. This low-cost, user-friendly AI tool offers a scalable solution for digital phenotyping, livestock monitoring, and informed selection in smallholder settings. The approach holds strong potential to support data-driven decision-making and sustainable productivity gains in tropical beef production systems.