RESUMO O estudo das propriedades físicas e químicas do solo é um procedimento de custo e tempo relativamente elevado. Na busca de alternativas para predizer esses atributos a partir de um número menor de amostras do solo, o uso de Redes Neurais Artificiais (RNA) tem sido apontado como uma técnica computacional com grande capacidade de resolver problemas por meio da experiência, e possuem a capacidade de aquisição e posterior aplicação deste conhecimento. Esse trabalho teve por objetivo utilizar a RNA para estimar os atributos físicos e químicos de solo. Os dados utilizados foram provenientes da análise física e química de solo, coletados em 120 pontos amostrais, os quais foram submetidos à análise descritiva, análise geoestatística, treinamento e análise das RNAs. Na análise geoestatística, para cada atributo do solo, foi verificado o modelo de semivariograma que apresentou melhor ajuste ao modelo experimental, e como método de interpolação foi usada técnica da krigagem ordinária. As RNAs foram treinadas, selecionadas considerando a assertividade no mapeamento dos padrões considerados e utilizadas na estimativa de todos dos atributos de solo. O erro médio de cada estimativa obtida pela técnica da krigagem ordinária foi comparado com o erro médio da estimativa obtida pela RNA e, posteriormente foram comparadas com os valores originais por meio do teste-t de Student. Os resultados mostram que a técnica de RNAs apresenta assertividade compatível à krigagem ordinária. O uso da técnica de RNA apresentou-se promissora para obter estimativas de atributos de solo empregando um número menor de amostras de solo. | ABSTRACT Soil physical and chemical analyses are relatively high-cost and time-consuming procedures. In the search for alternatives to predict these properties from a reduced number of soil samples, the use of Artificial Neural Networks (ANN) has been pointed out as a great computational technique to solve this problem by means of experience. This tool also has the ability to acquire knowledge and then apply it. This study aimed at using ANNs to estimate the physical and chemical properties of soil. The data came from the physical and chemical analysis of 120 sampling points, which were submitted to descriptive analysis, geostatistical analysis, and ANNs training and analysis. In the geostatistical analysis, the semivariogram model that best fitted the experimental variogram was verified for each soil property, and the ordinary kriging was used as an interpolation method. The ANNs were trained and selected based on their assertiveness in the mapping of considered standards, and then used to estimate all soil properties. The mean errors of ordinary kriging estimates were compared to those of ANNs and then compared to the original values using Student's t-Test. The results showed that the ANN had an assertiveness compatible with ordinary kriging. Therefore, such technique is a promising tool to estimate soil properties using a reduced number of soil samples.

Soil physical and chemical analyses are relatively high-cost and time-consuming procedures. In the search for alternatives to predict these properties from a reduced number of soil samples, the use of Artificial Neural Networks (ANN) has been pointed out as a great computational technique to solve this problem by means of experience. This tool also has the ability to acquire knowledge and then apply it. This study aimed at using ANNs to estimate the physical and chemical properties of soil. The data came from the physical and chemical analysis of 120 sampling points, which were submitted to descriptive analysis, geostatistical analysis, and ANNs training and analysis. In the geostatistical analysis, the semivariogram model that best fitted the experimental variogram was verified for each soil property, and the ordinary kriging was used as an interpolation method. The ANNs were trained and selected based on their assertiveness in the mapping of considered standards, and then used to estimate all soil properties. The mean errors of ordinary kriging estimates were compared to those of ANNs and then compared to the original values using Student's t-Test. The results showed that the ANN had an assertiveness compatible with ordinary kriging. Therefore, such technique is a promising tool to estimate soil properties using a reduced number of soil samples.