Automatic Recovery Estimation of Degraded Soils by Artificial Neural Networks in Function of Chemical and Physical Attributes in Brazilian Savannah Soil

• Autor(a) principal: Bonini Neto, A. [UNESP]
• Data de Publicação: 2019
• Outros Autores: Bonini, C. S. B. [UNESP], Reis, A. R. [UNESP], Piazentin, J. C. [UNESP], Coletta, L. F. S. [UNESP], Putti, F. F. [UNESP], Heinrichs, R. [UNESP], Moreira, A.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1080/00103624.2019.1635144; http://hdl.handle.net/11449/185890
• topic: Artificial intelligence; soil chemistry; soil physics; ranking; degraded soils

The Oxisols is predominant in 54% of Brazilian territories and characterized by high weathering, relatively low chemical properties, and adequate structure. This study aimed to analyze the Oxisols through an Artificial Neural Network (ANN) with the purpose of estimating its recovery in function to soil chemical and physical attributes. The chemical attributes considered were: pH, cation exchange capacity (CEC), base saturation (V%), phosphorus (P), magnesium (Mg2+), and potassium (K+) and for the physical attributes, bulk density, soil porosity and soil resistance to penetration. The ANN used in this study is the Multilayer Perceptron (MLP), composed of three layers, input, intermediate and the output and with backpropagation training algorithm (supervised training). The intermediate layer is composed by 10 neurons and the layer of exit by 1 neuron, which has a function of informing the levels of chemical recovery (high, medium and low chemical attributes of the soil) and soil physics (recovered, partially recovered or not recovered). From the results obtained by ANN showed that the network reached an adequate training, with low mean square error (MSE). Therefore, ANN is a powerful and automatic alternative for the recovery estimation of degraded soils.