Search Results (37)

Accumulation of salts in irrigated soils can be detrimental not only to growing crops but also to groundwater quality. Soil salinity should be regularly monitored, and appropriate irrigation at the required leaching rate should be applied to prevent excessive salt accumulation in the root zone, thereby improving soil fertility and crop production. We combined two frequency domain electromagnetic induction (FD-EMI) mono-channel sensors (EM31 and EM38) and operated them at different heights and with different coil orientations to monitor the vertical distribution of soil salinity in a salt-affected irrigated area in Kairouan (central Tunisia). Multiple measurement heights and coil orientations were used to enhance depth sensitivity and thereby improve salinity predictions from this type of proximal sensor. The resulting multi-configuration FD-EMI datasets were used to derive soil salinity information via inverse modeling with a recently developed in-house laterally constrained inversion (LCI) approach. The collected apparent electrical conductivity (ECa) data were inverted to predict the spatial and temporal distribution of soil salinity. The results highlight several findings about the distribution of salinity in relation to different irrigation systems using brackish water, both in the short and long term. The expected transfer of salinity from the surface to deeper layers was systematically observed by our FD-EMI surveys. However, the intensity and spatial distribution of soil salinity varied between different crops, depending on the frequency and amount of drip or sprinkler irrigation. Furthermore, our results show that vertical salinity transfer is also influenced by the wet or dry season. The study provides insights into the effectiveness of combining two different FD-EMI sensors, EM31 and EM38, for monitoring soil salinity in agricultural areas, thereby contributing to the sustainability of irrigated agricultural production. The inversion approach provides a more detailed representation of soil salinity distribution across spatial and temporal scales at different depths, and across irrigation systems, compared to the classical method based on soil samples and laboratory analysis, which is a point-scale measurement. It provides a more extensive assessment of soil conditions at depths up to 4 m with different irrigation systems. For example, the influence of local drip irrigation was imaged, and the history of a non-irrigated plot was evaluated, confirming the potential of this method. Full article

Soil salinity severely constrains agricultural productivity and soil health, particularly in arid and semi-arid regions. Conventional salinity assessment methods are labor-intensive, time-consuming, and spatially limited. This study developed a data-scarce workflow integrating proximal sensing (EM38-MK2), very high-resolution multispectral imagery, and machine learning to map soil salinity at field scale in the semi-arid Sehb El Masjoune area, central Morocco. A total of 26 soil samples were analyzed for Electrical Conductivity (EC), and 500 Apparent Electrical Conductivity (ECa) measurements were collected and calibrated using the field samples. Spectral and topographic covariates derived from Unmanned Aerial Vehicle (UAV) and PlanetScope imagery supported model training using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and a Stacked Ensemble Learning Model (ELM). Regression Kriging (RK) was applied to model residuals to improve spatial prediction. ELM achieved the highest accuracy (R² = 0.87, RMSE ≈ 4.15), followed by RF, which effectively captured nonlinear spatial patterns. RK improved PLSR accuracy (by 11.1% for PlanetScope, 13.8% for UAV) but offered limited gains for RF, SVR, and ELM. SHAP analysis identified topographic covariates as the most influential predictors. Both UAV and PlanetScope delineated similar saline–sodic zones. The study demonstrates the following: (1) a scalable, data-efficient workflow for salinity mapping; (2) model and RK performance depend more on algorithmic design than sensor type; (3) interpretable ML and spatial modeling enhance understanding of salinity processes in semi-arid systems. Full article

Understanding soil variability supports improved land use and soil security. This study aimed to generate uniform geophysical classes by integrating data from three proximal geophysical sensors with synthetic soil and satellite images using machine learning, proposing a soil survey protocol. Geophysical data—natural gamma-ray emissions (eU, eTh, K⁴⁰), magnetic susceptibility (κ), and apparent electrical conductivity (ECa)—were collected in Piracicaba, Brazil, and clustered into homogeneous geophysical-isoparameter classes. These classes were modeled alongside Synthetic Soil Images (SYSIs), Sentinel-2 (0.45–2.29 μm), Landsat (0.43–12.51 μm) imagery, and morphometric variables. Empirical validation compared the resulting geophysical-isoparameter map with conventional pedological and lithological maps. The Support Vector Machine (SVM) algorithm exhibited the best classification performance. Results demonstrated that geophysical sensors quantitatively and qualitatively capture soil attributes linked to formation processes and types. The geophysical-isoparameter map correlated well with pedological and lithological patterns. The proposed protocol offers soil scientists a practical tool to delineate soil and lithological units using combined sensor data. Promoting collaboration among pedologists, pedometric mappers, and remote sensing experts, this approach presents a novel framework to enhance soil survey accuracy and efficiency. Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article

Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN. Full article

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (EC_a) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of EC_a sensors, namely an EC_a contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and EC_a data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the EC_a measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of EC_a to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization. Full article

The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (EC_a) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R²) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R² of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R² of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way. Full article

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter—OM; phosphorous—P₂O₅; potassium—K₂O; the sum of the exchange bases—SEB; and cation exchange capacity—CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil. Full article

To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management. Full article

Electromagnetic induction (EMI) systems are used for mapping the soil’s electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data from EMI measurements. To mitigate these temperature drift effects, it is customary to perform a temperature drift calibration of the instrument in a temperature-controlled environment. This involves recording the apparent electrical conductivity (ECa) values at specific temperatures to obtain a look-up table that can subsequently be used for static ECa drift correction. However, static drift correction does not account for the delayed thermal variations of the system components, which affects the accuracy of drift correction. Here, a drift correction approach is presented that accounts for delayed thermal variations of EMI system components using two low-pass filters (LPF). Scenarios with uniform and non-uniform temperature distributions in the measurement device are both considered. The approach is developed using a total of 15 measurements with a custom-made EMI device in a wide range of temperature conditions ranging from 10 °C to 50 °C. The EMI device is equipped with eight temperature sensors spread across the device that simultaneously measure the internal ambient temperature during measurements. To parameterize the proposed correction approach, a global optimization algorithm called Shuffled Complex Evolution (SCE-UA) was used for efficient estimation of the calibration parameters. Using the presented drift model to perform corrections for each individual measurement resulted in a root mean square error (RMSE) of <1 mSm⁻¹ for all 15 measurements. This shows that the drift model can properly describe the drift of the measurement device. Performing a drift correction simultaneously for all datasets resulted in a RMSE <1.2 mSm⁻¹, which is considerably lower than the RMSE values of up to 4.5 mSm⁻¹ obtained when using only a single LPF to perform drift corrections. This shows that the presented drift correction method based on two LPFs is more appropriate and effective for mitigating temperature drift effects. Full article

In the current context of increasing costs of production factors, it is essential to optimize the management of available resources, seeking to incorporate technologies that improve knowledge of the variables involved in the agronomic production process. The aim of this study is to define and validate management zones (MZ) in a 3.3 ha vineyard located near Évora, in the South of Portugal. A contact sensor (“Veris 2000 XA”) was used to map soil apparent electrical conductivity (EC_a) and a precision altimetric survey of the field was carried out with a global navigation satellite system receiver (GNSS). The results of these surveys were submitted to geostatistical treatments that allowed the definition of three MZ (less, intermediate, and more productive potential). The validation of such MZ was carried out by laboratory analysis of soil samples (texture, pH, organic matter—OM, moisture content, phosphorous, potassium, exchange bases, and cation exchange capacity—CEC), measurements of soil compaction (cone index—CI) with an electronic cone penetrometer, and through indices (Normalized Difference Vegetation Index—NDVI, and Normalized Difference Water Index—NDWI) obtained by remote sensing (RS) using Sentinel-2 satellite images. All these parameters (soil parameters and RS indices) proved the validity of the MZ (of less, intermediate, and more productive potential) defined from the EC_a and altimetric survey. This validation attests to the interest of expeditious technological tools for monitoring EC_a as a fundamental step in implementing smart agronomic decision-making processes. Full article

Precise adjustments of farm management activities, such as irrigation and soil treatment according to site-specific conditions, are crucial. With advances in smart agriculture and sensors, it is possible to reduce the cost of water and soil treatment inputs but still realize optimal yields and high-profit returns. However, achieving precise application requirements cannot be efficiently practiced with spatially disjointed information. This study assessed the potential of using an electromagnetic induction device (EM38-MK) to cover this gap. An EM38-MK was used to measure soil apparent electrical conductivity (ECa) as a covariate to determine soil salinity status and soil water content θ post irrigation at four depth layers (Hz: 0–0.25 m; Hz: 0–0.75 m; Vz: 0.50–1 m). The inverse distance weighting method was used to generate the spatial distribution thematic layers of electrical conductivity. The statistical measures showed an R² = 0.87; r > 0.7 and p ≤ 0.05 on correlation of ECa and SWC. Based on the South African salinity class of soils, the area was not saline ECa < 200 mS/m. The EM38-MK can be used to estimate soil salinity and SWC variability using ECa as a proxy, allowing precise estimations with depths and in space. These findings provide key information that can aid in irrigation scheduling and soil management. Full article

Wheat is among the world’s most important agricultural crops, with winter wheat accounting for approximately 25.5% of the total agricultural crop in Lithuania. The unchangeable goal of crop production is to achieve good and economically beneficial crop yield, but such efforts are often based on conventional agrotechnological solutions, and excessive fertilization, which is uneconomical and negatively affects the soil, the environment, and human health. In order to produce a rich and high-quality cereal crop, scientists and farmers are increasingly focusing on managing the sowing and fertilization processes. Precision technologies based on spectrometric methods of soil and plant characterization can be used to influence the optimization of sowing and fertilizer application rates without compromising crop yield and quality. The aim of this study was to investigate the effect of site-specific seeding and variable-rate precision fertilization technologies on the growth, yield, and quality indicators of winter wheat. Experimental studies were carried out on a 22.4 ha field in two treatments: first (control)—SSS (site-specific seeding) + URF (uniform-rate fertilization); second—SSS + VRF (variable-rate precision fertilization) and 4 repetitions. Before the start of this study, the variability of the soil apparent electrical conductivity (ECa) was determined and the field was divided into five soil fertility zones (FZ-1, FZ-2, FZ-3, FZ-4, and FZ-5). Digital maps of potassium and phosphorus precision fertilization were created based on the soil samples. Optical nitrogen sensors were used for variable-rate supplementary nitrogen fertilization. The variable-rate precision fertilization method in individual soil fertility zones showed a higher (up to 6.74%) tillering coefficient, (up to 14.55%) grain yield, number of ears per square meter (up to 27.6%), grain number in the ear (up to 6.2%), and grain protein content (up to 12.56%), and a lower (up to 8.61%) 1000-grain weight on average than the conventional flat-rate fertilization. In addition, the use of the SSS + VRF method saved approximately 14 kg N ha⁻¹ of fertilizer compared to the conventional SSS + URF method. Full article

Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (EC_a), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and EC_a at multiple depths were collected from two experimental fields (referred as Field_Und, Field_Flat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CV_Und = 54.4%, CV_Flat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) EC_a sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest EC_a depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high EC_a/low elevation versus low EC_a/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in Field_Und revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high EC_a/low elevation; low EC_a/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios. Full article

Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. Although unmanned aerial vehicles (UAVs) can provide efficient mapping across the entire vineyard, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band, which makes the monitoring of GWS problematic. The goal of this study is to explore whether and which of the ancillary variables (vegetation characteristics, temporal trends, weather conditions, and soil/terrain data) may improve the accuracy of GWS estimation using multispectral UAV and provide insights into the contribution, in terms of direction and intensity, for each variable contributing to GWS variation. UAV-derived vegetation indices, slope, elevation, apparent electrical conductivity (EC_a), weekly or daily weather parameters, and day of the year (DOY) were tested and regressed against stem water potential (Ψstem), measured by a pressure bomb, and used as a proxy for GWS using three machine learning algorithms (elastic net, random forest regression, and support vector regression). Shapley Additive exPlanations (SHAP) analysis was used to assess the relationship between selected variables and Ψstem. The results indicate that the root mean square error (RMSE) of the transformed chlorophyll absorption reflectance index-based model improved from 213 to 146 kPa when DOY and elevation were included as ancillary inputs. RMSE of the excess green index-based model improved from 221 to 138 kPa when DOY, elevation, slope, EC_a, and daily average windspeed were included as ancillary inputs. The support vector regression best described the relationship between Ψstem and selected predictors. This study has provided proof of the concept for developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as providing individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality. Full article