Design of poultry farm disease detection system based on K-Nearest Neighbor Algorithm

Every year, poultry farms suffer great damage from avian influenza outbreaks. The outbreak of avian influenza lowers the egg production rate and has a great impact on the market price. Countries around the world are working simultaneously to prevent the spread of avian influenza. However, despite these efforts, avian influenza still outbreaks every year, resulting in large-scale deaths of chickens. Therefore, in this paper, we propose a disease detection system based on K-Nearest Neighbor Algorithm to prevent large-scale spread of avian influenza. We used decrease in feed intake and a decrease in egg laying rate, which are the main symptoms of avian influenza when it outbreaks, as the standard data for the system's decision. If avian influenza is suspected according to the data analysis result, a push message is sent to the farmer's cell phone, and the farmer checks the information on the area suspected of avian influenza through the application linked with the system and transmits it to the server of the national livestock quarantine system. This is how the system is designed to work. Through this disease detection system, we expect that it will be possible to prevent the spread of avian influenza to the surrounding areas and neighboring farms in advance and to contribute to preventing damage to farms.