Abstract
In this paper, a previously defined hybrid multilogistic regression model is extended and applied to a precision agriculture problem. This model is based on a prediction function which is a combination of the initial covariates of the problem and the hidden neurons of an Artificial Neural Network (ANN). Several statistical and soft computing techniques have been applied for determining these models such as logistic regression, ANNs and Evolutionary Algorithms (EAs). This paper proposes the use of Radial Basis Functions (RBFs) transformations for this model. The estimation of the coefficients of the model is basically carried out in two phases. First, the number of RBFs and the radii and centers’ vector are determined by means of an EA. Afterwards, the new RBF nonlinear transformations obtained for the best individual in the last generation are added to the covariate space. Finally, a maximum likelihood optimization method determines the rest of the coefficients of the multilogistic regression model. In order to determine the performance of this approach, it has been applied to a problem of discriminating cover crops in olive orchards affected by its phenological stage using their spectral signatures obtained with a high-resolution field spectroradiometer. The empirical results for this complex real agronomical problem and the corresponding Dunnet statistical test carried out show that the proposed model is very promising in terms of classification accuracy and number of wavelengths used by the classifier.
This work has been partially subsidized by the TIN2008-06681-C06-03 project of the Spanish Inter-Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P08-TIC-3745 project of the “Junta de Andalucía” (Spain).
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Gutiérrez, P.A., Hervás-Martínez, C., Fernández, J.C., López-Granados, F. (2009). Hybrid Multilogistic Regression by Means of Evolutionary Radial Basis Functions: Application to Precision Agriculture. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_29
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DOI: https://doi.org/10.1007/978-3-642-02319-4_29
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