AUTOMATIC CLASSIFICATION MODEL FOR LIVESTOCK SLAUGHTERING VIA ARTIFICIAL NEURAL NETWORKS
DOI:
https://doi.org/10.18011/bioeng2019v13n1p1-11Keywords:
Artificial intelligence, livestock, estimate, body mass indexAbstract
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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BITTENCOURT, C. D. R. Classificação automática do acabamento de gordura em imagens digitais de carcaças bovinas. Dissertação apresentada à Universidade de Brasília. 78 p. 2009.
BONINI NETO, A.; BONINI, C. S. B.; BISI, B. S.; DOS REIS, A. R.; COLETTA, L. F. S. Artificial neural network for classification and analysis of degraded soils. Revista IEEE América Latina, v. 15, n. 3, p. 503-509, 2017.
BRAGA, A. DE P.; CARVALHO, A. P. DE L. F. DE; LUDERMIR, T. B. Redes neurais artificiais: teoria e aplicações. 2. ed. Rio de Janeiro: LTC Editora, 2007.
GABRIEL FILHO, L. R. A.; CREMASCO, C. P.; PUTTI, F. F.; CHACUR, M. G. M. Application of fuzzy logic for the evaluation of livestock slaughtering. Engenharia Agrícola, Jaboticabal, v. 31, p. 813-825, 2011.
GABRIEL FILHO, L. R. A.; PUTTI, F. F.; CREMASCO, C. P.; DEYVER BORDIN, CHACUR, M. G. M.; GABRIEL, L. R. A. Software to assess beef cattle body mass through the fuzzy body mass index. Engenharia Agrícola, Jaboticabal, v. 36, n. 1, p. 179-193, 2016.
GARCIA, A. P.; CAPPELLI, N. L.; UMEZU, C. K. Electrically driven fertilizer applicator controlled by fuzzy logic. Engenharia Agrícola, Jaboticabal, v.34, n.3, p. 510-522, 2014.
HAGAN, M. T.; MENHAJ, M. B. Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neural Networks, v.5, n.6, p. 989-993, 1994.
ICMC. Disponível no site do ICMC da Universidade de São Paulo - USP em: <http://conteudo.icmc.usp.br/pessoas/andre/research/neural/index.htm#links> Acesso em 9 de agosto de 2017.
LIU, Y. Calibrating an industrial microwave six-port instrument using artificial neural network technique. IEEE Transactions on Instrumentation and Measurement, v.45, n.2, p. 651-656, 1996.
MATHWORKS. MATLAB (MATrix LABoratory) Disponível em: <http://www.mathworks.com>. Acesso em 21 de maio de 2017.
MCCULLOCH, W. S. e PITTS, W. A. Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, n.9, pp. 127-147, 1943.
PAGANO, M.; GAUVREAU, K. Princípios de Bioestatística. São Paulo: Cengage Learning, 506p. 2012.
PEREIRA, D. F.; BIGHI, C. A.; GABRIEL FILHO, L. R. A.; CREMASCO, C. P. C. System fuzzy for estimate of welfare of broiler breeders. Engenharia Agrícola, Jaboticabal, v.28, p. 624-634, 2008.
PUTTI, F. F.; GABRIEL FILHO, L. R. A.; SILVA, A. O.; LUDWIG, R.; GABRIEL, C. P. C. Fuzzy logic to evaluate vitality of catasetum fimbiratum species (Orchidacea). Irriga, Botucatu, v. 19, n.3, p. 405-413, 2014.
PUTTI, F. F.; GABRIEL FILHO, L. R. A.; CREMASCO, C. P. C.; BONINI NETO, A.; BONINI, C. S. B.; REIS, A. R. A Fuzzy mathematical model to estimate the effects of global warming on the vitality of Laelia purpurata orchids. Mathematical Biosciences, v. 288, p. 124–129, jun. 2017.
RUMELHART, D. E.; HINTON, G. E.; WILLIAMS, R. J. Learning representations by back-propagating errors. Nature, v. 323, p. 533, 9 out. 1986.
VENTURA, R. V.; SILVA, M. A.; MEDEIROS, T. H.; DIONELLO, N. L.; MADALENA, F. E.; FRIDRICH, A. B.; VALENTE, B. D.; SANTOS, G. G.; FREITAS, L. S.; WENCESLAU, R. R.; FELIPE, V. P. S.; CORRÊA, G. S. S. Uso de redes neurais artificiais na predição de valores genéticos para peso aos 205 dias em bovinos da raça Tabapuã. Arq. Bras. Med. Vet. Zootec., v.64, n.2, p.411-418, 2012.
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