Artificial neural networks on eggs production data management
Almeida, Luiz Gabriel Barreto deOliveira, Éder Barbosa deFurian, Thales QuediBorges, Karen ApellanisRocha, Daniela Tonini daSalle, Carlos Tadeu PippiMoraes, Hamilton Luiz de Souza
Background: Eggs have acquired a greater importance as an inexpensive and high-quality protein. The Brazilian eggindustry has been characterized by a constant production expansion in the last decade, increasing the number of housedanimals and facilitating the spread of many diseases. In order to reduce the sanitary and financial risks, decisions regarding the production and the health status of the flock must be made based on objective criteria. The use of Artificial NeuralNetworks (ANN) is a valuable tool to reduce the subjectivity of the analysis. In this context, the aim of this study was atvalidating the ANNs as viable tool to be employed in the prediction and management of commercial egg production flocks.Materials, Methods & Results: Data from 42 flocks of commercial layer hens from a poultry company were selected. Thedata refer to the period between 2010 and 2018 and it represents a total of 600,000 layers. Six parameters were selectedas output data (number of dead birds per week, feed consumption, number of eggs, weekly weight, weekly egg production and flock uniformity) and a total of 13 parameters were selected as input data (flock age, flock identification, totalhens in the flock, weekly weight, flock uniformity, lineage, weekly mortality, absolute number of dead birds, eggs/hen,weekly egg production, feed consumption, flock location, creation phase). ANNs were elaborated by software programsNeuroShell Predictor and NeuroShell Classifier. The programs identified input variables for the assembly of the networksseeking the prediction of the variables called outgoing that are subsequently validated. This validation goes through thecomparison between the predictions and the real data present in the database that was the basis for the work. Validation ofeach ANN is expressed by the specific statistical parameters multiple determination (R2) and Mean Squared Error...(AU)
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