Modeling of soil penetration resistance using statistical analyses and artificial neural networks

Abstract

ABSTRACT. An important factor for the evaluation of an agricultural system's sustainability is the monitoring of soil quality via its physical attributes. The physical attributes of soil, such as soil penetration resistance, can be used to monitor and evaluate the soil's quality. Artificial Neural Networks (ANN) have been employed to solve many problems in agriculture, and the use of this technique can be considered an alternative approach for predicting the penetration resistance produced by the soil's basic properties, such as bulk density and water content. The aim of this work is to perform an analysis of the soil penetration resistance behavior measured from the cone index under different levels of bulk density and water content using statistical analyses, specifically regression analysis and ANN modeling. Both techniques show that soil penetration resistance is associated with soil bulk density and water content. The regression analysis presented a determination coefficient of 0.92 and an RMSE of 0.951, and the ANN modeling presented a determination coefficient of 0.98 and an RMSE of 0.084. The results show that the ANN modeling presented better results than the mathematical model obtained from regression analysis.Keywords: modeling, soil physical properties, neural networks.Modelagem da resistência à penetração do solo usando análises estatísticas e redes neurais artificiais RESUMO. Um importante fator para a avaliação da sustentabilidade de sistemas agrícolas é o monitoramento da qualidade do solo por meio de seus atritutos físicos. Logo, atributos físicos do solo, como resistência à penetração, podem ser empregados no monitoramento e na avaliação da qualidade do solo. Redes Neurais Artificiais (RNA) tem sido empregadas na solução de vários problemas na agricultura, neste contexto, o uso desta técnica pode ser considerada uma abordagem alternativa para se predizer a resistência à penetração do solo a partir de suas propriedades básicas como densidade e teor de água. Portanto, o objetivo desse trabalho foi desenvolver um estudo do comportamento da resistência à penetração do solo, medida a partir do índice de cone, empregando análise de regressão e modelagem por RNA. Ambas as técnicas mostraram que a resistância à penetração do solo está associada com a densidade e o teor de água do solo. A análise de regressão apresentou coeficiente de regressão de 0,92 e REMQ igual a 0,951 enquanto a modelagem por RNA apresentou coeficiente de determinação de 0,98 e REMQ igual a 0.084. Os resultados indicaram que a modelagem por RNA apresentou melhores resultados do que o modelo matemático obtido a partir da análise de regressão.Palavras-chave: modelagem, propriedades físicas do solo, redes neurais.

Artificial neural network model for simulation of water distribution in sprinkle irrigation

“…In this study, the dataset of the 108 trials was normalized in the interval from 0 to 1, according to Chagas et al (2010), and randomly divided into subsets: training subset data (98 trials) and validation subset data (10 trials); the latter was used for the discussion of the results of the model. As in Santos et al (2012), the strategy of cross-validation was employed in this study, with 20% of the training data used to estimate the performance of the ANN and determine the moment to interrupt the training, in order to avoid excessive training.…”

“…ANNs can be applied in many areas, like soil digital mapping based on soil-landscape relationships (Arruda et al, 2013), classification of degradation levels of pastures (Chagas et al, 2009), rainfall erosivity (Moreira et al, 2006, estimation of reference evapotranspiration through air temperature data (Alves Sobrinho et al, 2011), modeling of soil penetration resistance (Santos et al, 2012) or even identification and classification of soybean cultivars by planting region (Galão et al, 2011).…”

Modeling and Correlation of Soil Cone Index for Bulk Density, Moisture Content and Penetration Depth Levels in a Sandy Loam Soil

“…Gubiani et al (2011) found that the parameterized regression models are not general and cannot be readily applied to other soils because of significant variations in MC and BD among different soils properties due to disparities in texture, pore size distribution or particle density. Santos et al (2012) employed anartificial neural networks model for predicting the penetration resistance produced by the soil's basic properties, such as BD and MC. They showed that CI is associated with BD and MC.…”

Application of Computational Intelligence Methods for Predicting Soil Strength

“…The soil cone index could also be a function of soil electrical conductivity (Abbaspour-Gilandeh et al, 2011). Santos et al (2012) used statistical analyses and ANNs for predicting soil penetration resistance based on the soil bulk density and water content. Results showed that ANNs architecture 2-2-2-1 presented an RMSE (Root Mean Square Error) of value less than 0.085, an R 2 equal to 0.98 and a global mean error of approximately 6.75%, whereas the model obtained from statistical analyses presented an RMSE of 0.951 and an R 2 of 0.92.…”