Artificial neural networks in the prediction of soil chemical attributes using apparent electrical conductivity

Samuel A Silva,Arlicélio Q Paiva,Railton O Santos,Caique C Medauar,Daniel M Queiroz,Julião S S Lima

doi:10.5424/sjar/2021193-17600

Abstract

Aim of study: To use artificial neural networks (ANN) to predict the values and spatial distribution of soil chemical attributes from apparent soil electrical conductivity (ECa) and soil clay contents.Area of study: The study was carried out in an area of 1.2-ha cultivated with cocoa, located in the state of Bahia, Brazil.Material and methods: Data collections were performed on a sampling grid containing 120 points. Soil samples were collected to determine the attributes: clay, silt, sand, P, K+, Ca2+, Mg2+, S, pH, H+Al, SB, CTC, V, OM and P-rem. ECa was measured using the electrical resistivity method in three different periods related to soil sampling: 60 days before (60ECa), 30 days before (30ECa) and when collecting soil samples (0ECa). For the prediction of chemical and physical-chemical attributes of the soil, models based on ANN were used. As input variables, the ECa and the clay contents were used. The quality of ANN predictions was determined using different statistical indicators. Thematic maps were constructed for the attributes determined in the laboratory and those predicted by the ANNs and the values were grouped using the fuzzy k-means algorithm. The agreement between classes was performed using the kappa coefficient.Main results: Only P and K+ attributes correlated with all ANN input variables. ECa and clay contents in the soil proved to be good variables for predicting soil attributes.Research highlights: The best results in the prediction process of the P and K+ attributes were obtained with the combination of ECa and the clay content.

Highlights

The soil is a dynamic and highly variable system and its surface layers are more delicate and dynamic when compared to the rest of its matrix (Daniel et al, 2003)
The use of a large sample density puts a burden on the precision agriculture (PA) system, making it unfeasible in some cases depending on the availability of financial resources to be contributed at this stage, especially for those attributes whose determination requires complex laboratory analyses
Among the sensors used in PA, the ones used to measure the apparent electrical conductivity (ECa) of the soil stand out, whose spatial variability is highly correlated with that of different soil attributes (Grubbs et al, 2019)

Summary

Introduction

The soil is a dynamic and highly variable system and its surface layers are more delicate and dynamic when compared to the rest of its matrix (Daniel et al, 2003). In precision agriculture (PA), the description of the variables that characterize soil fertility must be carried out from different attributes and, mainly, from a high number of samples for the same attribute (Silva & Lima, 2012). The use of a large sample density puts a burden on the PA system, making it unfeasible in some cases depending on the availability of financial resources to be contributed at this stage, especially for those attributes whose determination requires complex laboratory analyses. Among the sensors used in PA, the ones used to measure the apparent electrical conductivity (ECa) of the soil stand out, whose spatial variability is highly correlated with that of different soil attributes (Grubbs et al, 2019)

Objectives

Methods

Results