2022
DOI: 10.3390/life12111722
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Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach

Abstract: Predicting yield is essential for producers, stakeholders and international interchange demand. The majority of the divergence in yield and essential oil content is associated with environmental aspects, including weather conditions, soil variety and cultivation techniques. Therefore, aniseed production was examined in this study. The categorical input variables for artificial neural network modelling were growing year (two successive growing years), growing locality (three different locations in Vojvodina Pro… Show more

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Cited by 9 publications
(7 citation statements)
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“…The elements of matrices were estimated in the course of calculation in the ANN models development. Their values were adjusted by applying optimization techniques to minimize the error between the networks and experimental outputs (Pezo et al 2022) according to the sum of squares (SOS).…”
Section: Assessment Of the Impact Of Landfill Size Municipality Size ...mentioning
confidence: 99%
“…The elements of matrices were estimated in the course of calculation in the ANN models development. Their values were adjusted by applying optimization techniques to minimize the error between the networks and experimental outputs (Pezo et al 2022) according to the sum of squares (SOS).…”
Section: Assessment Of the Impact Of Landfill Size Municipality Size ...mentioning
confidence: 99%
“…These calculations were performed according to the weight coefficients of the developed ANN models [ 34 ]. The given equation was utilized to evaluate the direct influence of the input parameters on the output variables, taking into account the weighting coefficients incorporated within the Artificial Neural Network (ANN) models [ 35 ]: where w represents the weights of the ANN model, i is the input variable, j is the output variable, k is the hidden neuron, n is the number of hidden neurons, and m is the number of inputs.…”
Section: Methodsmentioning
confidence: 99%
“…These calculations were performed according to the weight coefficients of the developed ANN models [34]. The given equation was utilized to evaluate the direct influence of the input parameters on the output variables, taking into account the weighting coefficients incorporated within the Artificial Neural Network (ANN) models [35]:…”
Section: Global Sensitivity Analysismentioning
confidence: 99%
“…The negative skewness values and relatively lower kurtosis indicate a left-skewed distribution, suggesting less pronounced values. These metrics play a key role in describing data shape, providing valuable insights into symmetry, tails, and the presence of outliers or extreme values [71].…”
Section: Artificial Neural Network Modelmentioning
confidence: 99%