AUTOMATIC CLASSIFICATION MODEL FOR LIVESTOCK SLAUGHTERING VIA ARTIFICIAL NEURAL NETWORKS

Authors

  • A. Bonini Neto
  • C.S.B. Bonini
  • F.F. Putti
  • M. Campos
  • L.R. Gabriel Filho
  • M.G.M. Chacur
  • J. C. Piazentin

DOI:

https://doi.org/10.18011/bioeng2019v13n1p1-11

Keywords:

Artificial intelligence, livestock, estimate, body mass index

Abstract

Nowadays, the search for tools that facilitate and even replace human work have gained a great worldwide prominence. Artificial neural networks (ANNs) are one of these tools, since they present a great power of applications, especially when it comes to data classification, pattern recognition, image analysis, among others. The objective of this work was to develop a tool for automatic classification of ruminant animals by means of an Artificial Neural Network (ANN) of three layers. This network is known as Multilayer Perceptron (MLP), here feed forward type (no feedback) and backpropagation training algorithm with supervised training. The idea was to identify the slaughter groups and those that require more intensive feeding, using as input variables the mass and height and as output variable, body mass index (BMI). The data used in this study were obtained from a herd of 147 Nelore cows, located in the city of Santa Rita do Pardo - Mato Grosso do Sul (MS). From the results, the network obtained an excellent performance in the training phase (100 samples), with mean square error around 10-5. Already at the diagnostic phase (network operation), the remaining 47 Nellore cows data that did not participate in the network training were submitted to the network, of these results, the network presented, on average, an error around 0.6% in relation to the desired output (normalized data), which resulted in an error of 1 sample of the 47 analyzed.

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Published

2019-03-30

How to Cite

BONINI NETO, A.; BONINI, C.; PUTTI, F.; CAMPOS, M.; GABRIEL FILHO, L.; CHACUR, M.; PIAZENTIN, J. C. AUTOMATIC CLASSIFICATION MODEL FOR LIVESTOCK SLAUGHTERING VIA ARTIFICIAL NEURAL NETWORKS. Revista Brasileira de Engenharia de Biossistemas, Tupã, São Paulo, Brazil, v. 13, n. 1, p. 1–11, 2019. DOI: 10.18011/bioeng2019v13n1p1-11. Disponível em: https://seer.tupa.unesp.br/index.php/BIOENG/article/view/755. Acesso em: 27 jan. 2022.

Issue

Section

Regular Articles