Alexey Ruchay, Hao Guo, Andrea Pezzuolo
This systematic review analyzes and synthesizes advancements in automated monitoring technologies for cattle drinking behavior and water intake measurement from 2000 to 2025. Using a thorough search strategy across 121 selected articles, the study summarizes various technological applications, such as radio frequency identification (RFID), accelerometers, water flow meters, electronic drinkers, and advanced vision-based systems integrated with AI (including Deep Learning and Generative AI). These innovations enable the precise acquisition of individual animal data, enhance predictive capabilities for health and welfare, and contribute to scalable water management solutions. Critically synthesizing this diverse literature provides a holistic understanding of the technological landscape and identifies key strategic directions for future development. For instance, RFID systems can record heifer movements at water points with 95% accuracy. Accelerometer algorithms can detect over 94% of drinking events lasting more than 10 s. Advanced vision-based systems, particularly those utilizing DeepLabCut and LSTM, have achieved recognition accuracies of up to 98.25% for beef cattle drinking behavior. Water flow meters demonstrate a consistent correlation of 0.99 with measured volumes. Additionally, artificial neural networks have demonstrated prediction accuracies of approximately 94.3% for water consumption on dairy farms. Despite significant progress, critical issues remain. These include limitations in accurately classifying short drinking events, reliability challenges in extensive environments, and the high costs of implementing advanced systems. There are persistent knowledge gaps regarding the optimal water requirements of cattle under various environmental conditions. This challenge is exacerbated by current technological limitations in accurately measuring individual water intake in extensive grazing systems and consistently classifying brief drinking events. Continued development of validated sensors and advanced analytics is crucial for transforming livestock farming into more sustainable practices. Further research is necessary to develop robust, cost-effective, and scalable solutions for global food security and environmental stewardship.