Machine Learning Model for Assuring Bird Welfare during Transportation

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: AgriEngineering

سال: 2022

ISSN: ['2624-7402']

DOI: https://doi.org/10.3390/agriengineering4020025