Abstract
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.
Highlights
Precision agriculture (PA) presents promising perspectives for crop management aimed at increasing productivity and optimizing production process, besides reducing environmental impacts from agricultural practices
We have considered an isotropic spatial dependence, i.e. when spatial dependence is the same in all directions
The trained artificial neural networks acquired the necessary knowledge to estimate the results of the analyzed soil properties, regardless of its spatial dependence
Summary
Precision agriculture (PA) presents promising perspectives for crop management aimed at increasing productivity and optimizing production process, besides reducing environmental impacts from agricultural practices. Knowing the spatial variability of soil properties allows us to describe a joint correlation of these variables, besides being fundamental for farming management (OLIVEIRA; FERNANDES; TEIXEIRA, 2011). The spatial continuity of a variable can be characterized by the similarity of its contents at two neighboring points in space. Such feature derives from a central tendency measure and/or a certain degree of spatial dependence in which close observations are associated, being greater in shorter distances. The number of samples to be collected in the field to represent properly the distribution of soil properties is a frequent question among PA users. Since sampling is a costly and lengthy method, it often becomes unfeasible in practice (SOUZA et al, 2014)
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