Machine Learning Model for Assuring Bird Welfare during Transportation

Abstract

Bird welfare and comfort is highly impacted by extreme environments, including hot/cold temperatures, relative humidity, and heat production within the coops during loading at the farm, transportation, and holding at the processing plants. Due to the complexity of the multiphysics phenomena involving fluid flow, heat transfer, and multispecies mixtures (humidity) within the coops, machine learning models may be helpful to evaluate broiler welfare under various environments. Machine learning techniques (Artificial Neural Networks and Bayesian Optimization) were applied to estimate the desired parameters required to ensure broiler welfare inside the coops. Artificial Neural Networks (ANNs) were trained with the results of Computational Fluid Dynamics (CFD) simulations for various ranges of inputs related to the microenvironment. Input variables included air velocity, broiler heat production, ambient temperature, and relative humidity. The Output variable was the Enthalpy Comfort Index (ECI), which is a measure of the bird welfare. The trained networks were then analyzed using Bayesian Optimization (BO) for the inverse mapping of ANNs and to predict the range of acceptable input parameters for a desired output, i.e., ECI in the comfort level. Results indicate that reducing the broilers heat production inside the coop along with increasing fan velocity enhances the broiler welfare and the thermal microenvironment. The BO developed in this study provide the microenvironmental parameters to estimate the bird welfare that is comfortable.