International Journal of Science, Engineering and TechnologyAuthors: Gauri Vijay Shinde, Aarti Santosh Shinde, Manisha Rajendra Shinde, Dr. Shubhangi Gunjal, Professor Bangar A.P, Professor Bhosale S.B.
Abstract: Poultry farms traditionally rely on non-digital, reactive monitoring practices. This results in a delay in detecting unfavourable environmental conditions. In turn, this leads to increased bird deaths and economic loss. The Intelligent Poultry Farm Management System (IPFMS) is a project that has been designed for the commercial broiler farm that is an IOT and machine learning based project. The system utilizes five types of sensors which are MQ135 (ammonia), DHT11/BME280 (temperature and humidity from the feed presentation), load cell (feed weight), and HC-SR04 (water level). So, these sensors connect to the ESP32 microcontroller and transmit real-time data. Moreover, it connects to MQTT Server (broker) which is an instance/service hosted in the cloud. So, the service ultimately provides an Apache/MySQL back-end. Three ML algorithms are employed for distinct prediction tasks: to forecast the ammonia levels, we use linear regression; for classifying risks and safety, we use decision tree; and logistic regression is used to compute the probabilities of the various risks. The farmers are notified for each tier (Normal, Warning, Critical) through a real-time web & mobile dashboard. The end-to-end latency is less than 1.5 seconds. A connected e-commerce module allows farm level transactions. Tests conducted on a live commercial broiler farm with 5000 birds, over 30 days found the sensor visible 99.7% of the time, Zero Critical false alerts, 92.9% Warning Tier Correct alert in a Commercial biosecurity farm set-up. The comparative study shows that IPFMS achieves better accuracies, alerts, and functionalities than existing IoT-only and single-model approaches, thereby providing a real-life and scalable blueprint for smart poultry farming 2.0.
