Rural areas are undergoing a smart transformation. Unlike the outdated image of the past, rural regions are now becoming arenas for technological competition through the application of advanced innovations. Smart farms and smart livestock farming, powered by AI, big data, and IoT, are prime examples. Among these, smart livestock farming has recently come to the forefront. Compared to large-scale crop farming (average 1.55ha, approx. 4,500 pyeong), building a system for livestock farming is more feasible. This change is particularly necessary due to the continuous decline of the rural population and rapid aging of farmers.
[Fig] Example of Smart Livestock Farming
The core technologies of smart livestock farming are feeding management and data-driven livestock prediction (diagnosis and care). Feeding management involves environmental control using temperature and humidity sensors inside and outside barns, CCTV monitoring, and automatic feeders. Data-driven prediction technologies utilize biometric sensing, such as livestock body temperature and behavior patterns, combined with barn environmental data, to enable early disease detection and optimize rearing environments. Currently, domestic smart livestock farming remains at the stage of monitoring and controlling livestock and environmental factors.
[Fig] Conceptual Diagram of Smart Livestock Technology
A smart livestock company, Robos (CEO Jae-Hyun Park), has developed a smart slaughtering robot technology based on vision image data of slaughtered animals. Recognized for this innovation, the company successfully secured a 700 million KRW (Pre-A) investment. Robos’s robot estimates the optimal cutting positions for the pubic bone and sternum based on vision image data and executes precise cuts accordingly. This technology enables automation in slaughtering processes that were previously dependent on human labor. With this investment, Robos plans to advance beyond robotics to develop a comprehensive automated slaughtering solution, ultimately realizing full unmanned operations.
[Fig] Robos’s Automated Slaughtering Robot
Technological development for smart livestock farming is also active. Korea Livestock Data (CEO Noh-Gyeom Kyung) is leading this field by developing AI-based solutions that provide individual animal health management and prevent infectious diseases in barns. Pusan National University has also developed a deep-learning-based system that grades livestock products automatically using image data, ensuring objectivity. Such efforts highlight the active pace of technological development in smart livestock farming.
[Fig] Korea Livestock Data’s Smart Livestock Solution ‘FarmsPlan’
The government supported the adoption of smart livestock barns in 2022 for 5,750 households, which represent 22% of full-time farmers in the livestock sector (pigs, poultry, Hanwoo, dairy). Support focused on farm management programs that track breeding, disease, and management information. Additional support included automation equipment such as automatic feeders, water management systems, feed measurement devices, shipment sorters, and automatic nursing machines, all aimed at enabling remote control. These initiatives illustrate the government’s diverse measures to transform traditional livestock farming into smart livestock farming.
#SmartLivestock #AIinLivestock #DigitalLivestock #LivestockRobotics #AutomationInLivestock
Rural areas are undergoing a smart transformation. Unlike the outdated image of the past, rural regions are now becoming arenas for technological competition through the application of advanced innovations. Smart farms and smart livestock farming, powered by AI, big data, and IoT, are prime examples. Among these, smart livestock farming has recently come to the forefront. Compared to large-scale crop farming (average 1.55ha, approx. 4,500 pyeong), building a system for livestock farming is more feasible. This change is particularly necessary due to the continuous decline of the rural population and rapid aging of farmers.
[Fig] Example of Smart Livestock Farming
The core technologies of smart livestock farming are feeding management and data-driven livestock prediction (diagnosis and care). Feeding management involves environmental control using temperature and humidity sensors inside and outside barns, CCTV monitoring, and automatic feeders. Data-driven prediction technologies utilize biometric sensing, such as livestock body temperature and behavior patterns, combined with barn environmental data, to enable early disease detection and optimize rearing environments. Currently, domestic smart livestock farming remains at the stage of monitoring and controlling livestock and environmental factors.
[Fig] Conceptual Diagram of Smart Livestock Technology
A smart livestock company, Robos (CEO Jae-Hyun Park), has developed a smart slaughtering robot technology based on vision image data of slaughtered animals. Recognized for this innovation, the company successfully secured a 700 million KRW (Pre-A) investment. Robos’s robot estimates the optimal cutting positions for the pubic bone and sternum based on vision image data and executes precise cuts accordingly. This technology enables automation in slaughtering processes that were previously dependent on human labor. With this investment, Robos plans to advance beyond robotics to develop a comprehensive automated slaughtering solution, ultimately realizing full unmanned operations.
[Fig] Robos’s Automated Slaughtering Robot
Technological development for smart livestock farming is also active. Korea Livestock Data (CEO Noh-Gyeom Kyung) is leading this field by developing AI-based solutions that provide individual animal health management and prevent infectious diseases in barns. Pusan National University has also developed a deep-learning-based system that grades livestock products automatically using image data, ensuring objectivity. Such efforts highlight the active pace of technological development in smart livestock farming.
[Fig] Korea Livestock Data’s Smart Livestock Solution ‘FarmsPlan’
The government supported the adoption of smart livestock barns in 2022 for 5,750 households, which represent 22% of full-time farmers in the livestock sector (pigs, poultry, Hanwoo, dairy). Support focused on farm management programs that track breeding, disease, and management information. Additional support included automation equipment such as automatic feeders, water management systems, feed measurement devices, shipment sorters, and automatic nursing machines, all aimed at enabling remote control. These initiatives illustrate the government’s diverse measures to transform traditional livestock farming into smart livestock farming.
#SmartLivestock #AIinLivestock #DigitalLivestock #LivestockRobotics #AutomationInLivestock