Decoding Bovine Communication with AI and Multimodal Systems ~ Advancing Sustainable Livestock Management and Precision Agriculture

Achieving sustainability in livestock farming requires advanced, non-invasive monitoring systems that enhance both productivity and animal welfare. Traditional methods for assessing dairy cow ingestive behavior, such as manual observation and sensor-based tracking, are often limited in scalability and accuracy. This study advances precision livestock farming by integrating multimodal artificial intelligence (AI) to decode bovine vocalizations in real time. Our approach leverages acoustic recordings, video analysis, and biometric sensor data to create a comprehensive system capable of detecting subtle patterns in feeding behavior and physiological well-being. By employing Generative AI and Large Language Models, our framework not only classifies ingestive behaviors but also interprets vocal signals linked to stress, health, and environmental conditions. The extracted features are transformed into spectrograms and fused with biometric indicators, enabling early detection of anomalies. This information is delivered through an intuitive dashboard, empowering farmers with real-time insights to optimize feeding strategies, reduce resource wastage, and mitigate welfare concerns. Unlike conventional deep learning approaches, which struggle with environmental variability, our system adapts dynamically across diverse farm settings, ensuring robustness and generalizability. This work directly contributes to global sustainability goals by improving resource efficiency, enhancing dairy herd management, and reducing the environmental footprint of livestock production. By integrating cutting-edge AI with practical farm applications, we pave the way for a more intelligent, responsive, and ethical approach to animal agriculture--where technology serves as a bridge between scientific advancements and on-farm decision-making.