Weight Dynamics Analysis: A Novel Approach for Timely Growth Observation of Poultry at Commercial Open-Shed Broiler Farms

This scholarly paper focuses on timely identification of poultry weight conditions throughout various growth phases, employing the potent tools of artificial intelligence and machine learning at a renowned commercial open-shed broiler farm. This research contributes valuable insights and strategies for effective management and care across diverse developmental stages of poultry by harnessing the capabilities of advanced technologies. This article focuses on the early prediction of poultry weight by implementing a data-driven machine-learning algorithm called Multiple Linear Regression. The essential data is gathered through an advanced smart poultry device equipped with various sensors, including Temperature, Humidity, NH3, CO2, and Dust sensors. This data can be carefully studied and then used to predict the weight of the poultry as they grow. After implementing the Multiple Linear Regression application, the authors obtained a set of critical graphs that provides valuable insights into the co-relations between various factors and is able to predict poultry weight. The paper concludes by discussing that poultry growth and weight negligence have far-reaching consequences for poultry farmers, economics, feed efficiency, disease susceptibility, consumer demand, and long-term sustainability. Real-time monitoring and data analysis empower farmers with insights for optimal conditions and precision feeding.