Identificação de Madeiras utilizando a Espectrometria no Infravermelho Próximo e Redes Neurais Artificiais

Oliveira, Azauri Albano de; Siqueira, Paulo Henrique; Nisgoski, Silvana; Muñiz, Graciela Inés Bolzón de; Ferreira, J.H.

doi:10.5540/tema.2015.016.02.0081

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Three feedforward layers (input, hidden layer, and output layer) and supervised training were employed with the Levenberg-Marquardt backpropagation training algorithm, which is considered the fastest method for networking (Barbosa et al, 2005). MSE was used for the performance function of the model, in which, for the output of the neuron, the sigmoidal tangent activation function was selected (Ferraz et al, 2014;Oliveira et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Artificial Neural Networks for Prediction of Physiological and Productive Variables of Broilers

et al. 2020

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Due to a number of factors involving the thermal environment of a broiler cutting installation and its interaction with the physiological and productive responses of birds, artificial intelligence has been shown to be an interesting methodology to assist in the decision-making process. For this reason, the main aim of this work was to develop an artificial neural network (ANN) to predict feed conversion (FC), water consumption (Cwater), and cloacal temperature (tclo) of broilers submitted to different air dry-bulb temperatures (24, 27, 30, and 33ºC) and durations (1, 2, 3, and 4 days) of thermal stress in the second week of the production cycle. Relative humidity and wind speed were fixed at 60% and 0.2 ms -1 , respectively. The experimental data were used for the development of an ANN with supervised training using the Levenberg-Marquardt backpropagation algorithm. The ANN consisted of three input layers one hidden, and three output with sigmoidal tangent transfer functions with values between -1 and 1. The developed ANN has adequate predictive capacity, with coefficients of determination (R 2 ) for tclo, FC, and Cwater of 0.79, 0.87, and 0.97, respectively. In this way, the proposed ANN can be used as a support for decision-making to trigger poultry heating systems for broiler breeding.

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Section: Methodsmentioning

confidence: 99%

Artificial Neural Networks for Prediction of Physiological and Productive Variables of Broilers

et al. 2020

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“…This technique has been effectively used to discriminate species using wood and leaf samples , Espinoza et al 2012, Nisgoski et al 2015a, and data for classification can be analysed after different pre-treatment methods (Tominaga 1999, Tsuchikawa et al 2003, Oliveira et al 2015. For Eucalyptus, Castillo et al (2008) used NIR spectroscopy for fast discrimination of Eucalyptus globulus and E. nitens.…”

Section: Use Of Visible and Near-infrared Spectroscopy F O R D I S C mentioning

confidence: 99%

Use of Visible and Near-Infrared Spectroscopy for Discrimination of Eucalypt Species by Examination of Solid Samples

Nisgoski¹,

Muniz²,

Gonçalves³

et al. 2017

JTFS

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Eucalypt species have high importance in the forestry sector in Brazil. The wood has wide range of uses, including pulp, charcoal, plywood panels and treated boards. However, eucalypt species are hard to identify in lumber, which can cause problems in industrial use. So, the aim of this study was to combine visible and near-infrared spectroscopy to discriminate wood samples of two Corymbia spp. and five Eucalyptus spp. Visible spectroscopy was performed according to the CIELab standard. The data were analysed using descriptive statistics and regression analysis. Near-infrared analyses were based on ASTM E1655-05. The data were analysed with the Unscrambler X chemometric program, in the raw form and after pre-processing with second derivative of Savitzky-Golay. Visible and near-infrared spectroscopy can be successfully applied to separate Eucalyptus and Corymbia species. In the visible spectroscopic analyses, it was possible to separate the wood samples into two colour groups, i.e. red/rose and grey/yellow/brown. In the near-infrared spectra it was possible to discriminate samples, and three groups associated with density of the species were formed. Both techniques were appropriate to distinguish species and their industrial use might solve practical problems.

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“…Wood species are distinctively identified by the physical aspects of the tree, such as the trunk shape, leaves and flowers (Ibrahim et al, 2017). When wood alone is available, the analysis assumes greater complexity, requiring time and knowledgeable specialists and needs to be performed based on the macro-and microscopic characteristics (Oliveira et al, 2015). Hence, species identification from wood alone is heavily dependent upon the specialists.…”

Section: Introductionmentioning

confidence: 99%

“…According to Paula et al (2014) these alternatives can be distinguished into spectroscopy-dependent techniques and image analysis-dependent techniques. Among the first group, the works of Piuri & Scotti (2010), Oliveira et al (2015), and Nisgoski et al (2017a, b) rank high. Among the image-analysis studies, those of Khalid et al (2008), Wang et al (2013), Yusof et al (2013), Paula et al (2014), Martins et al (2015), Zamri et al (2016), and Ibrahim et al (2017) are eminent.…”

Section: Introductionmentioning

confidence: 99%

Potential of Texture Analysis for Charcoal Classification

et al. 2019

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Charcoal produced from reforested wood can be distinguished from the charcoal derived from the wood of native species. This identification is very important for the trade, control and monitoring of charcoal production in Brazil. This study investigated the potential of texture analysis for classifying the charcoal based on origin (eucalyptus or native) and species. A total of 17 wood species were studied, five of which belonged to genus Eucalyptus and 12 were native to the Zona da Mata Mineira. Texture features based on the gray level co-occurrence matrix were extracted from digital images. The linear discriminant analysis was used to classify the images with these features. Employing 10 features, 96.2% accuracy was achieved for the classification by origin and 90.4% for the categorization by species. Texture analysis was shown to be a favorable and effective method that could facilitate the establishment of semiautomated techniques to classify the charcoal based on origin or species.

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Identificação de Madeiras utilizando a Espectrometria no Infravermelho Próximo e Redes Neurais Artificiais

Cited by 7 publications

References 6 publications

Artificial Neural Networks for Prediction of Physiological and Productive Variables of Broilers

Artificial Neural Networks for Prediction of Physiological and Productive Variables of Broilers

Use of Visible and Near-Infrared Spectroscopy for Discrimination of Eucalypt Species by Examination of Solid Samples

Potential of Texture Analysis for Charcoal Classification

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