Creating a Digital Twin and IoT-Enabled Reinforcement Learning Algorithm to Optimize the Health Status of Poultry Production in Agriculture

Aliyu Abubakar, Mutari Hajara Ali, Tijjani Hassan Darma

Abstract


The integration of advanced technologies, such as digital twins and IoT, has the potential to transform the poultry industry by enabling real-time monitoring and optimization of health conditions. This study focuses on developing a digital twin model supported by IoT data streams and a reinforcement learning algorithm to enhance the health management of poultry in modular farming systems. The goal is to automate the adjustment of environmental parameters, including temperature, humidity, CO2 concentration, and feeding schedules, using a learning approach that adapts over 1,000 iterations to achieve optimal health status. Graphical representations of these 1,000 iterations indicate a health status of 80.5% for poultry production, revealing significant improvements in health metrics. This demonstrates that the combined use of digital twins and reinforcement learning can effectively respond to dynamic conditions, enhancing productivity and welfare. The proposed solution showcases the practical benefits of technology-driven farming by reducing manual intervention and improving overall poultry health.


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References


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