Development of a Rapid Soil Water Content Detection Technique Using Active Infrared Thermal Methods for In-Field Applications

Abstract

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory–based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity.

Hyperspectral Visible–Near Infrared Determination of Arsenic Concentration in Soil

“…For the partial least squares (PLS) (Wold, Sjostrom, and Erikssonn 2001;Antonucci et al 2011), the procedure included (1) extraction of raw spectra (X block); (2) extraction of measured values (Y block); 3) data fusion of the two datasets (X and Y blocks) in one analysis dataset (AS); (4) sample set partitioning based on joint X and Y blocks (SPXY) (Harrop Galvao et al 2005) separation of the AS into two subsets, one (MS) for the model (75%) and one (TS) for the independent validation test (25%); (5) application of preprocessing algorithms to both X and Y; (6) application of PLS (modeling and testing); (7) calculation of efficiency parameters of prediction [bias error; root mean square error (RMSE); standard error of prevision (SEP); correlation coefficient (r); ratio of percentage deviation (RPD) following the RPD classification of Williams (1987) and variable importance in the projection (VIP scores)]. The best model was selected yielded maximum r and RPD (calculated to RMSE of test subset) and minimum SEP and bias error for prediction of As concentration.…”

Estimation of the moisture content of tropical soils using colour images and artificial neural networks

“…Several of these methods use physical soil properties, such as temperature, electrical resistance, capacitance, spectrometry, or the apparent dielectric constant, to indirectly estimate soil moisture (Altendorf et al, 1999;Anderson and Croft, 2009;Antonucci et al, 2011;Calamita et al, 2012;Francesca et al, 2010;Imhoff et al, 2007;Lihua et al, 2005;Noborio, 2001;Souza and Matsura, 2002). However, many of the methods that make use of such properties, although efficient, have relatively high costs that limit their widespread use.…”

Contribution of thermal imaging to fertigation in soilless culture

“…The technology can be utilized specifically to monitor the efficiency of water resource use for both field applications [11] and potted plants in a soilless culture [12,13]. The leaf temperature of plants is the result of both external and internal (physiological) factors.…”