Prediction of mean surface temperature of broiler chicks and load microclimate during transport

Abstract

ABSTRACT This study aimed to determine a model to predict mean surface temperature of broiler chicks and live load microclimate conditions during transport by using neural networks. The research was conducted in the state of São Paulo, Brazil, by monitoring nine shipments with different density of boxes using an air-conditioned truck with an average capacity of 380 boxes. Fourteen chick boxes were chosen on each shipment, assessing five chicks per box. The mean surface temperature of chicks (MST) was measured with an infrared thermometer in both loading and unloading. By assessing the container microclimate (center and inside boxes), air temperature (T), relative humidity (RH) and specific enthalpy (h) were recorded; thereby, seventeen data loggers were placed, one per box (14), and three along the container. MST and truck microclimate were analyzed using artificial neural networks with a single layer and seven neurons, which were trained with the least mean square (LMS) algorithm. MST in the unloading showed a better prediction of MST during transport. The best prediction of microclimatic conditions was obtained inside the boxes during the shipment.

Keywords

• poultry
• ambience
• neural networks
• air-conditioned truck