# AI-Based Smart Monitoring Framework for Livestock Farms
## 1 Introduction  
Agriculture holds significant importance for purposes such as food supply and the preservation of environmental ecosystems. Additionally, the livestock industry, as a highvalue-added sector, is expected to account for a substantial portion of agricultural demand. The ongoing decline in rural populations and the reluctance to engage in agricultural work are expected to exacerbate labor shortages and cause management crises in the livestock industry. In Europe, the livestock equipment industry is transitioning from hardware-based to software-integrated operations, with the active development of products using farm integration management technologies and advanced sensing systems. By incorporating software functions, it is possible to maximize hardware performance, reduce farm labor, and manage precise specifications. European livestock equipment manufacturers are increasingly developing integrated management technologies to enhance the usability and applicability of their products across farming operations. In line with the smart era, efforts are being undertaken to improve productivity and increase income in the livestock industry by integrating advanced ICT convergence technologies and optimizing livestock management [1]. As part of the construction of intelligent livestock management systems, research is being conducted to develop smart farm models that are tailored to each livestock type [2,3]. Smart livestock farming leverages technologies such as the Internet of Things (IoT) and artificial intelligence (AI) to simultaneously improve agricultural productivity and optimize the required workforce [2]. In these applications, research on the situational awareness of animals plays a critical role. The domestic and international smart farm markets are experiencing annual growth rates of over 10%, while the smart farm market in the livestock sector is also expected to grow significantly. Smart agriculture, based on ICT convergence technologies, is entering the stage of foundational technology development and growth; Figure 1 illustrates the three generations of domestic livestock smart farms that the National Institute of Animal Science has categorized: first-generation basic models, second-generation advanced models, and third-generation cutting-edge models. Currently, most domestic livestock farms implementing smart farm technologies are at the 1 st or 1.5 intermediary level. This indicates the pressing need for progress toward second-generation and third-generation livestock smart farms to fully realize the benefits of these advanced technologies. As the adoption of smart livestock systems continues to expand among livestock farms, research is being conducted to establish data use frameworks that enhance the competitiveness of farms. These frameworks focus on standard-based data collection and analysis, as well as data-driven livestock management, enabling diverse forms of data application. However, compared to European nations that are advanced in livestock management, such as the Netherlands, Denmark, and Belgium, which have established the concept of precision livestock farming similar to smart farming and are actively pursuing research and dissemination, domestic efforts remain in the early stages. Figure 1: Domestic smart livestock farm model (National Institute of Animal Science in Korea, 2022). In Europe, research has been actively performed to monitor and control environmental factors within livestock facilities, such as temperature, humidity, and harmful gases. Using ubiquitous technology, automated systems have been developed to regulate lighting and the opening or closing of roofs during rainy conditions. Advanced systems have been created to allow large-scale farms to control their facilities in real time while viewing video feeds. These technologies also include fuzzy systems to reason about environmental changes, control ventilation systems, and trigger alarms in hazardous situations caused by environmental changes, thereby promoting advanced livestock practices.