Estatísticas dos resultados das análises de laboratório de solo

Filho, Alberto Cargnelutti; Storck, Lindolfo; Bartz, Hardi Rene Departamento de Solos

doi:10.1590/s0103-84781996000300010

Cited by 2 publications

(1 citation statement)

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“…Soil chemical and physical analyses are the principal sources of data for agriculture. This information allows to better manage the carbon cycle (Guevara et al, 2020), plants growth (Soong et al, 2020), soil acidity and fertility (Cargnelutti Filho et al, 1996), and is part of the soil security framework (Amundson et al, 2015;Bennett et al, 2019). However, those analyses are performed in traditional soil laboratories that produce potential toxic residuals for the environment.…”

Section: Introductionmentioning

confidence: 99%

Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil

Mendes¹,

Boechat²,

Gualberto³

et al. 2021

Revista Brasileira De Ciência Do Solo

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Soil chemical and physical analyses are the major sources of data for agriculture. However, traditional soil analyses are time-consuming, not cost-efficient, and not environmentally friendly. An alternative to traditional soil analyses is soil spectroscopy. This technique is a low-cost and quick analytical method, which can be implemented in a laboratory and/or in-situ. Nevertheless, some spectrometers are expensive and do not contemplate the entire spectrum. Despite this limitation, the main objective of the study was to create a soil spectral library of the Piauí State using only the 1000-2500 nm range. In this sense, it was evaluated and standardized the soil spectral library by accessing the combination of smoothing, standard normal variate, continuum removal, and Savitzky-Golay derivative spectral preprocessing procedures with partial least squares, random forest, and cubist machine learning algorithms. It was collected 262 geo-referenced soil samples at the layer of 0.00-0.20 m across the entire Piauí State, representing most of its soil variability. The soil properties evaluated were pH(H 2 O), sand, clay, and soil organic carbon (SOC) contents. This study demonstrated that the Standard Normal Variate was one of the most promising preprocessing procedures to improve model predictions for pH(H 2 O), sand, and clay. For SOC and pH, the best overall results were without preprocessing the soil spectra. Moreover, the cubist model was the most accurate in predicting soil properties. Finally, our study showed evidence of the potential and feasibility of using this soil spectral library to estimate soil properties such as pH(H 2 O), sand, clay, and SOC.

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