Seleção de bandas espectrais apoiada pela metaheurística PSO para predição do teor de alumínio trocável de amostras de solo

Abstract

The soil nutrient content estimation by diffuse reflectance spectroscopy is done through a prediction model whose performance determines the method effectiveness when performing it. This model is elaborated by techniques that try correlating a sample collection’s reflectance data to the respective reference value obtained through chemical analysis, both arranged as dataset attributes. Nevertheless, the dataset attributes amount is large – high dimensionality – and not all of them are relevant to the interest nutrient’s prediction, so elaborating a model from a dataset with these characteristics involves some complications that impact its prediction performance. A strategy to circumvent them is keeping only relevant attributes to the interest nutrient’s prediction, which is done through Feature Subset Selection (FSS), but the majority of algorithms that perform it do not operate satisfactorily when handling highdimensional sets. On the other hand, the pertinent literature found that employing evolutionary algorithms for FSS in high-dimensionality datasets provides quality subsets in an acceptable execution time, so this master thesis’ objective was to identify with Particle Swarm Optimization – PSO – metaheuristic support the relevant wavelengths of visible and near infrared region for exchangeable aluminum content prediction of Campos Gerais region soil samples. For this, the FSS was configured as an optimization problem which the objective was to minimize the AIC value of candidate subsets models elaborated by Multiple Linear Regression algorithm. In addition, knowing the algorithm parameters influence on its final result, first the ideal values for iterations number, swarm size and threshold value that provided the selection of best subsets were investigated, then these subsets were validated in an independent dataset and the best established. Our results suggest that in our scenario 40 iterations, swarm size 20 and threshold 0.6 provided the best subsets, but the prediction performance of the best model is amenable to improvement. The dimensionality reduction provided by the adopted method was significant, so this approach is recommended for FSS in spectroscopy datasets.