Improving Decision-Making in Broiler Production: A Novel Model Using Paraconsistent Annotated Evidential Logic Eτ for Environmental Control

As the poultry industry grows, maintaining effective environmental control presents increasing challenges, particularly in tropical climates where variability undermines data reliability. Temperature, relative humidity, air velocity, and gas concentrations directly affect broiler health, productivity, and welfare. Although IoT-based monitoring technologies have enhanced data collection, conventional systems remain limited in managing inconsistent, incomplete, or contradictory sensor readings caused by abrupt fluctuations, equipment failures, or animal interference. This study presents a novel, mathematically grounded model for environmental monitoring in broiler production, leveraging Paraconsistent Annotated Evidential Logic Eτ to process conflicting data without discarding potentially valuable information. The model assigns dynamic degrees of favorable and unfavorable evidence to each reading and integrates a temporal decay function to reduce the influence of outdated data. This approach enables consistent inference even when faced with contradictory or delayed sensor data inputs. The results demonstrate that the proposed model effectively manages inconsistent sensor data, enhancing decision-making in dynamic and uncertainty-prone production environments. The approach improves the robustness and accuracy of environmental assessments, supports timely interventions, and contributes to better animal welfare, productivity, and operational efficiency. Beyond its practical contributions, the model illustrates the applicability of non-classical logic in smart agriculture, offering a flexible framework for decision support in farming contexts. The research also supports SDGs 3, 9, and 12, promoting sustainable, innovative, and welfare-focused poultry production.