Artificial Neural Network for Classification and Analysis of Degraded Soils

• Autor(a) principal: Bonini Neto, A. [UNESP]
• Data de Publicação: 2017
• Outros Autores: Bonini, C. S.B. [UNESP], Bisi, B. S. [UNESP], Coletta, L. F.S. [UNESP], Dos Reis, A. R. [UNESP]
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/TLA.2017.7867601; http://hdl.handle.net/11449/169544
• topic: Artificial intelligence; Intelligent systems; Recovery of soil; Soil physics

This study aimed to evaluate the Artificial Neural Network (ANN) to establish a classification and analysis of degraded soils and its recovery in response to lime and gypsum application. The analyzed degraded soil was classified as Oxisol, and the physical attributes considered were: soil density, soil porosity (macroporosity and microporosity) and soil penetration resistance. The ANN used in this study is the backpropagation composed of two layers, the middle layer and the output layer, with supervised training. The network has four inputs, that are the physical attributes of the soil, in the middle layer the network contains ten neurons and the output layer only one neuron, which has the function of informing if the soil was recovered (R), partially recovered (PR) or not recovered (NR). The analyzed data come from the year 2012, concerning the depths 0.0-0.1 m, 0.1-0.2 m and 0.2-0.4 m. Considering the performance of ANN, it was verified that the network obtained an adequate training to classify the degraded soils, showing low mean square error of analyzed data. Therefore, ANN is considered an interesting alternative and a powerful automatic tool to classify degraded soils during recovery process.