IISPF:IOT Integrated Smart Poultry Farm Using k-NN Algorithm

Chicken farms have been crucial throughout human history in supplying nourishment for the growing population. A proper setting is key for fowl development, keeping illness at bay, and efficient yield. High temperature and moisture levels encourage bacterial multiplication, resulting in the creation of ammonia (NH3) by way of organic matter breakdown. Ammonia (NH3) and Nitrogen (CO2) are dangerous gases that may bring about fowl sickness and mortality. The joining of Machine Learning (ML) and the Internet of Things (IoT) delivers an effective real-time oversight arrangement. Via this integration of machine learning and the internet of things, chick disease can be foretold, and the provision of feed and water, together with warmth, can be made automatic. Dangerous gases such as ammonia and nitrogen are observed by sensors. When the amount of ammonia and nitrogen gas rises in the air, automated neutralizers start up to work against the detrimental gases. The setup is made to be a live automation monitor and to show fowl farm atmospheric states like warmth, feed level, water level, hot condition, and cool condition. For feeding, chicks utilize a tray with a linked ultrasonic sensor. If the chick feed level is under the marked spot, the ultrasonic sensor gives alerts to the chicks feed box, and feed is released into the tray. If the chick feed level is over the marked spot, the ultrasonic sensor signals the chicks feed box, and the feed distributing ceases. When the water level is lower than the designated area, the ultrasonic sensor sends alerts to the motor, and water starts to run into the water tray. When the water level is greater than the designated area, the ultrasonic sensor signals the motor, and the water flow stops. Fans are set up to adjust warmth within the fowl farm. To lessen high warmth, two fans are arranged in opposing directions, forcing air inward. To lessen cool warmth, two fans are arranged in opposing directions, forcing air outward. The setup is made for live oversight and to anticipate fowl farm atmospheric states using machine learning. Regular care, automatic warnings, and optimized feed and water direction further support a sound fowl-raising setting. Data gathered from sensors is stored in database platforms such as Firebase cloud, and the database info is linked to an app.