Soil Moisture Measurements Research Articles

Seasonal water source patterns in a northern Arizona pine forest

The relationships among stand density and seasonal plant water source patterns in ponderosa pine (Pinus ponderosa) forests are important for informed management decisions in a bimodal climate with increasing variability in winter and monsoon precipitation inputs. Winter precipitation recharges soil moisture, yet it has declined over the past 20 years in the southwestern United States, and monsoon precipitation is becoming more variable in both magnitude and timing. Near Flagstaff, Arizona in August 2013 (monsoon), October 2013 (post-monsoon), and May 2014 (post winter snow melt), we measured soil moisture, soil water δD at five depths, and xylem water δD in Muhlenbergia montana (a perennial C4 grass), Festuca arizonica (a perennial C3 grass), and P. ponderosa seedlings (< 2 years old), saplings (2–5 cm basal diameter), and mature trees (> 60 cm diameter at breast height) in treated (thinned and burned) and untreated (no thinning, no burning) stands. We found that soil moisture was higher at all soil depths in treated stands in May, after snow melt, and this pattern persisted through August in the deepest soil (60 cm). We also found that, in all sampling months, δD in xylem water of grasses and pine seedlings indicated use of shallower soil water than for pine saplings and mature trees, presumably due to differences in rooting depths. Additionally, in August, δD in xylem water of pine saplings and mature trees indicated greater reliance on a deeper water source in untreated stands than in treated stands, likely due to greater competition for shallow water in untreated stands. Our isotopic data indicate that grasses and seedlings used predominantly monsoon water in August and October, while pine saplings and mature trees used predominantly winter water during all sampling months. Importantly, our data indicate that regenerating trees (seedlings and saplings) used both winter and monsoon seasonal water sources, suggesting an increasingly important role for monsoon precipitation if winter precipitation inputs continue to decline. Collectively, these findings demonstrate that management actions can benefit forests via increased soil moisture, that overstory trees rely predominantly on winter precipitation, and that monsoon precipitation is important for herbaceous species and younger, regenerating overstory trees.

Hybrid CNN-SVM Classifier Approaches to Process Semi-Structured Data in Sugarcane Yield Forecasting Production

Information communication technology (ICT) breakthroughs have boosted global social and economic progress. Most rural Indians rely on agriculture for income. The growing population requires modern agricultural practices. ICT is crucial for educating farmers on how to be environmentally friendly. It helps them create more food by solving a variety of challenges. India’s sugarcane crop is popular and lucrative. Long-term crops that require water do not need specific soil. They need water; the ground should always have adequate water due to the link between cane growth and evaporation. This research focuses on forecasting soil moisture and classifying sugarcane output; sugarcane has so many applications that it must be categorized. This research examines these claims: The first phase model predicts soil moisture using two-level ensemble classifiers. Secondly, to boost performance, the proposed ensemble model integrates the Gaussian probabilistic method (GPM), the convolutional neural network (CNN), and support vector machines (SVM). The suggested approach aims to correctly anticipate future soil moisture measurements affecting crop growth and cultivation. The proposed model is 89.53% more accurate than conventional neural network classifiers. The recommended models’ outcomes will assist farmers and agricultural authorities in boosting production.

A Study on Self Watering Magnetic Plants

The main idea of this article is to show you how Normal operation of the system automatic watering is in progress places for these common plants. . Many studies have been conducted, which have been discussed in several articles and in the system designed to measure soil moisture.

Deep Learning-Based Improved WCM Technique for Soil Moisture Retrieval with Satellite Images

The water cycle around the globe is significantly impacted by the moisture in the soil. However, finding a quick and practical model to cope with the enormous amount of data is a difficult issue for remote sensing practitioners. The traditional methods of measuring soil moisture are inefficient at large sizes, which can be replaced by remote sensing techniques for obtaining soil moisture. While determining the soil moisture, the low return frequency of satellites and the lack of images pose a severe challenge to the current remote sensing techniques. Therefore, this paper suggested a novel technique for Soil Moisture Retrieval. In the initial phase, image acquisition is made. Then, VI indexes (NDVI, GLAI, Green NDVI (GNDVI), and WDRVI features) are derived. Further, an improved Water Cloud Model (WCM) is deployed as a vegetation impact rectification scheme. Finally, soil moisture retrieval is determined by the hybrid model combining Deep Max Out Network (DMN) and Bidirectional Gated Recurrent Unit (Bi-GRU) schemes, whose outputs are then passed on to enhanced score level fusion that offers final results. According to the results, the RMSE of the Hybrid Classifier (Bi-GRU and DMN) method was lower (0.9565) than the RMSE of the Hybrid Classifier methods. The ME values of the HC (Bi-GRU and DMN) were also lower (0.728697) than those of the HC methods without the vegetation index, the HC methods without the presence of water clouds, and the HC methods with traditional water clouds. In comparison to HC (Bi-GRU and DMN), the HC method without vegetation index has a lower error of 0.8219 than the HC method with standard water cloud and the HC method without water cloud.

The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam.

Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there are hardly any monitoring methods that use wireless underground transmission to monitor the water content inside earth dams. Real-time monitoring of changes in the soil moisture content can more directly determine the water level of seepage. Wireless transmission of sensors buried underground requires signal transmission through the soil medium, which is more complex than traditional air transmission. Henceforth, this study establishes a wireless underground transmission sensor that overcomes the distance limitation of underground transmission through a hop network. A series of feasibility tests were conducted on the wireless underground transmission sensor, including peer-to-peer transmission tests, multi-hop underground transmission tests, power management tests, and soil moisture measurement tests. Finally, field seepage tests were conducted to apply wireless underground transmission sensors to monitor the internal seepage water level before an earth dam failure. The findings show that wireless underground transmission sensors can achieve the monitoring of seepage water levels inside earth dams. In addition, the results supersede those of a conventional water level gauge. This could be crucial in early warning systems during the era of climate change, which has caused unprecedented flooding events.

Tree canopy cover and elevation affect the distribution of red harvester ant nests in a peri-urban setting.

With an increase in human population over the past 30 years, regional land use in south Texas has shifted from grassland and shrubland to a peri-urban matrix. Despite this shift from natural areas to more anthropogenically modified habitats, native red harvester ants (Pogonomyrmex barbatus) have maintained nest sites within parts of these matrices. To determine which habitat characteristics in a peri-urban landscape may play a role in red harvester ant nest site selection, we mapped the location of nests in 2020 and 2021. We then evaluated nest presence and absence relative to elevation, percentage of surrounding impervious surfaces, distance to roadways, and tree canopy cover (using NDVI). For a sub-sample of the study site, we also measured soil moisture and estimated the potential foraging area per colony with Voronoi tessellation. We found that nests were clustered together near high human-use areas such as athletic fields, lawns, sidewalks, and railroad tracks. Nests were more likely to be found in areas with higher elevation and lower tree canopy cover, with no impact from surrounding impervious surfaces or soil moisture. In fact, many nests were observed immediately adjacent to roadways and in paved parking lots. Red harvester ants are highly adept at nesting in disturbed, urbanized matrices, but still appear to be constrained by certain environmental factors like shading, potential flood risk (elevation), and access to food resources (foraging area).

Verticulture Plant Monitoring System Using Internet of Things Based on Blynk Application

The development of technology is now very rapid. One of them is the use of the internet of things in various fields. One of the applications of technology that is developing in the world including in Indonesia is in agriculture. In this field, there is a model of how to grow plants verticulturally that utilizes limited land. In agriculture, the verticulture cropping technique known as vertical cropping requires optimal soil moisture through proper watering. So an automatic monitoring process is needed to measure soil moisture, air humidity to plant temperature. In this study focused on optimizing the automatic monitoring system using the blynk application compared to ordinary monitoring. This research also encourages further exploration of the use of micro controllers and internet of things technology in agriculture.

Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network

AbstractCosmic‐ray neutron sensors (CRNS) have been used in many studies for measuring soil moisture and snow pack over intermediate scales. Corrections for barometric pressure and atmospheric humidity are well established, however, corrections for the effect of solar activity on neutron intensity rates have been overly simplistic, typically relying on one neutron monitor station and accounting for latitude and elevation crudely or not at all. Recognizing the lack of a generalized and scientifically robust approach to neutron intensity correction, we developed a new approach for correcting CRNS count rates based on analysis of data from 110 quality‐controlled neutron monitor stations from around the world spanning more than seven decades. Count rates from each monitor were plotted against the count rates from Climax, CO, USA or Jungfraujoch, Switzerland depending on the time period covered. Relationships between relative counting rates at the site of interest versus the reference neutron monitors were found to be strongly linear. The slope and intercept of these linear relation are both dependent on a scaling factor referred to as , which was shown to increase with increasing geomagnetic latitude and elevation. This dependence was represented using an empirical relationship based on a single reference neutron monitor. This generalized approach enables to be derived for any location on Earth and also lends itself to roving CRNS studies. The correction procedure also includes a location‐dependent normalization factor which enables easy substitution of an alternative reference neutron monitor into the correction procedure.

Burning trees in frozen soil: Simulating fire, vegetation, soil, and hydrology in the boreal forests of Alaska

Boreal ecosystems account for 29% of the world's total forested area and contain more carbon than any other terrestrial biome. Over the past 60 years, Alaska has warmed twice as rapidly as the contiguous U.S. and wildfire activity has increased, including the number of fires, area burned, and frequency of large wildfire seasons. These recent and rapid changes in climate and wildfire have implications for future vegetation composition, structure, and biomass in interior Alaska, given that the vegetation is highly dependent on active layer thickness, soil moisture, organic layer depth, and plant-available nutrients. Here we developed a new succession extension (DGS) of the LANDIS-II forest landscape model which integrates a vegetation dynamics model (NECN) with a soil carbon model (DAMM-McNiP), a hydrologic model (SHAW), and a deep soil profile permafrost model (GIPL) in a spatially-explicit framework. DGS Succession uses the algorithms in the NECN Succession extension of LANDIS-II to simulate growth, mortality and reproduction of vegetation but has three major improvements. First, the simple bucket model in NECN was replaced with a physically-based model (SHAW) that simulates energy and water fluxes (e.g. snow depth, evapotranspiration, soil moisture) at multiple levels in the canopy and soil. Second, the active, slow, and passive soil pools in NECN were replaced by seven soil pools that are measurable in the field, with carbon and nitrogen dynamics dictated by DAMM-McNiP. Finally, soil temperature and soil moisture are simulated only at one depth in NECN, but in DGS, soil temperature (and hence permafrost dynamics) are simulated at as many as 50 user-defined depths down to 4 m with SHAW and 75 m with GIPL. During the initial calibration phase, DGS was applied at three inventory sites at the Bonanza Creek Long Term Ecological Research area in Interior Alaska where climate forcings, species biomass, soil temperature, and/or soil moisture were available. For the landscape-scale simulations, DGS was run with the SCRPPLE fire extension of LANDIS-II under two scenarios of climate using a ∼400,000 ha landscape that included the inventory sites. Across all three sites, DGS generally captured the variation in soil moisture and temperature across depths, seasons, and years reasonably well, though there were some discrepancies at each site. DGS had better agreement with field measurements of soil moisture and temperature than its predecessor NECN which produced unrealistically low soil moisture and unrealistically high seasonal fluctuations in soil temperature. At the landscape scale, ignitions, area burned, and soil temperature increased under climate change, as expected, while soil moisture was relatively unchanged across climate scenarios. Biomass tended to decline under climate change, which differs from other modeling studies in this region but is consistent with the browning trends observed from remote sensing data. Simulating climate, vegetation succession, hydrology, permafrost, carbon and nutrient cycling, and wildfire in an integrated, spatially-explicit framework like LANDIS-II will allow us to disentangle the drivers and ecosystem responses in this rapidly changing ecosystem, as well as other forested systems with complex hydrologic, biochemical, cryospheric, and vegetation feedbacks.

Soil water depletion patterns in rainfed apple orchards and wheat fields.

Agricultural production in the Weibei rainfed highland, Northwest China, is challenged by severe drought and water shortages. While the land use pattern has shifted gradually from crop production to orchard farming in Weibei, little is known about the influence of fruit industry development on regional water resources and the rationality of planting orchards. Here, we characterized soil water depletion patterns in rainfed orchards and farmlands to evaluate the occurrence of soil desiccation under land use conversion from farmlands to orchards in Weibei. Soil moisture dynamics were monitored in the 0-150cm soil profiles of different aged Red Fuji apple orchards (young: 7 years, mature: 13 years, old: 22 years) and long-term cultivated winter wheat fields. We measured soil moisture content by oven-drying method in the middle of each month during the growing season of apple trees (March-September 2019). The over-depletion and depletion of soil water were analyzed to evaluate water stress and differential water depletion by distinct vegetation, respectively. The soil desiccation index was used to determine the occurrence of dry soil layers. Water stress was only observed at the 0-70-cm soil depths in the old orchards (mid-June) and farmlands (mid-May-mid-July). Water depletion took place at deeper depths for longer periods in the older orchards than in the younger orchards. Soil desiccation was absent in the young orchards, with mild desiccation at the 0-80-cm soil depths in the mature and old orchards in mid-June. The desiccation intensity was mild at the 0-60-cm soil depths in mid-April-mid-May, intense at the 0-150-cm soil depths in mid-June, and moderate at the 20-150-cm soil depths in mid-July. Results of this study demonstrate the mitigation of water stress and soil desiccation following conversion from wheat fields to apple orchards, which verifies the rationality of planting orchards in the rainfed highland area. Our findings provide strong support for developing a novel model of agro-industrial development, ecological construction, and sustainable economy in the vast arid and semi-arid areas of Northwest China.

Optimization of the number and locations of the calibration stations needed to monitor soil moisture using distributed temperature sensing systems: A proof-of-concept study

The single-probe heat-pulse (SPHP) technique combined with the Fiber-optic Distributed Temperature Sensing (DTS) technology can offer novel high-resolution measurements of soil moisture (θ) over spatial scales ranging from several centimeters to several kilometers. However, the key limitation of this method is in obtaining the calibration relationship between θ and soil thermal conductivity (λ) across a specific field. In a previous study, a new methodology using a Gaussian processes model was presented to account for the spatial variability in the λ-θ relationship. The model aggregated θ measurements from soil moisture sensors scattered over the SPHP transect with the corresponding DTS λ measurements at their locations. In this study, a novel methodology is tested to optimize the number and locations of soil moisture sensors required to account for the spatial variability of the λ-θ relationship to achieve higher accuracy from the SPHP technique. The proposed methodology utilizes hierarchical clustering to analyze the information contained in the spatial structure of the SPHP measurements as the soil dries from a nearly-saturated condition. The proposed methodology was tested using data from a field in Oklahoma. Monte-Carlo simulation was performed to validate the performance of the proposed methodology. The predictions obtained from the proposed methodology resulted in θ measurements accuracy comparable to those obtained from the 10% best Monte-Carlo iterations of randomly assigned soil moisture locations. This study demonstrates that the proposed methodology is more efficient than the traditional practice of randomly spreading calibration soil moisture sensors along the SPHP transect.

A Pixel-Scale Measurement Method of Soil Moisture Using Ground-Penetrating Radar

Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In this study, a measurement method of soil moisture using ground-penetrating radar (GPR) was proposed for pixel scale. We used a PulseEKKOTM PRO GPR system with 250 MHz antennas to measure soil moisture by Fixed Offset (FO) method in four 30 × 30 m2 plots chosen from the desert steppe. This study used a random combination method to analyze soil moisture measurements acquired by different numbers of GPR survey lines. The results showed that two survey lines of GPR would be sufficient under confidence level of 90% with the relative error of 7%, and four survey lines of GPR would be eligible under confidence level of 95% with the relative error of 5% for each plot. GPR measurement can reproduce the spatial distribution of soil moisture with higher resolution and smaller error, especially when two and four survey lines are designed in cross shape and grid shape, respectively. The method was applied to ground validation for the soil moisture from Landsat 8, showing the advantages of stable relative errors, less contingency and reliable evaluation when compared to point measurements. This method is fast and convenient and not limited to a certain pixel, and thus largely benefits the scale matching of remote sensing results and field measurements in ground validation.

Ratio Drought Index (RDI): A soil moisture index based on new NIR-red triangle space

ABSTRACT Accurate monitoring of soil moisture and the development of timely interventions are important to reduce the social and economic losses caused by drought. Compared to short-wave infrared (SWIR) and thermal infrared (TIR), near-infrared (NIR) and visible bands are widely used in almost all optical satellites. Drought monitoring using NIR and visible bands is therefore more relevant for optical satellites. Among the visible bands, the red band is often used in combination with the NIR band for drought monitoring due to its sensitivity to vegetation. However, current drought indexes based on the NIR and the red band applied to areas of high vegetation suffer from insufficient accuracy or tedious calculations. In this study, the ratio drought index (RDI) was developed after constructing a new feature space by examining the spectral properties of soil and vegetation at different water levels in the NIR and red bands. The accuracy of soil moisture inversion under two types of bare soil and vegetation was evaluated using in situ data from Tai’an City, Shandong Province. The perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) were also used to compare for the RDI. The results showed that the RDI correlation coefficients (R2) of 0.653 and 0.641 were better than the MPDI of 0.616 and 0.594 and the PDI of 0.602 and 0.546 for soil moisture measurements from vegetation and bare soil cover. The RDI attenuates the effect of vegetation on soil moisture inversion, as its root mean square error (RMSE) in vegetated areas is lower than that of the PDI and MPDI. The RDI calculation can be used as a theoretical guide for large-scale soil moisture estimation because it is fast, accurate and does not require additional quantitative remote sensing inversion factors.

MARSplines-Based Soil Moisture Sensor Calibration

Soil moisture sensor calibration based on the Multivariate Adaptive Regression Splines (MARSplines) model is studied in this paper. Different from the generic polynomial fitting methods, the MARSplines model is a non-parametric model, and it is able to model the complex relationship between the actual and measured soil moisture. Rao-1 algorithm is employed to tune the hyper-parameters of the calibration model and thus the performance of the proposed method is further improved. Data collected from four commercial soil moisture sensors is utilized to verify the effectiveness of the proposed method. To assess the calibration performance, the proposed model is compared with the model without using the temperature information. The numeric studies prove that it is promising to apply the proposed model for real applications.

МОНИТОРИНГ ГИДРОТЕРМИЧЕСКОГО РЕЖИМА ПОЧВОГРУНТОВ С ПОМОЩЬЮ ИНСТРУМЕНТА SMAT-METER ДЛЯ ИЗМЕРЕНИЯ ПРОФИЛЕЙ ВЛАЖНОСТИ И ТЕМПЕРАТУРЫ ПОЧВЫ

An instrument developed for measuring soil moisture and temperature profiles (Soil Moisture And Temperature meter-SMAT-meter) to a depth of 50 cm is described. The instrument is equipped with a solar battery and a data transmission system via a cellular network. The equipment is designed for automatic monitoring of the hydrothermal regime of soil in forest or rural areas, as well as in the areas close to the industrial or transport infrastructure.

RF-Self-Powered Sensor for Fully Autonomous Soil Moisture Sensing

Soil moisture monitoring and irrigation scheduling are essential parameters in farming efficiency. Internet-of-Things (IoT) technology is a promising solution for automating irrigation procedures and improving farming efficiency by removing human faults. In this article, a new method is introduced to measure soil moisture level along with providing energy to run a low-power transmitter as an alarm signal. A combination of a metamaterial perfect absorber (MPA) and two rectifiers that are designed at different frequencies specifies 5% and 25% soil moisture levels. The sensor monitors the soil moisture continuously without consuming energy. Once the soil moisture becomes 5% of the first rectifier starts working and provides 65 uW dc output, while the second rectifier is off. Increasing the soil moisture to 25%, the second rectifier creates 100 uW dc output when the first rectifier is off. The designed structure is fabricated on RO4003 in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array. The measurement results are provided by performing a set of different experiments. Initially, the MPA’s absorption characteristics are validated facing different polarization and incident angles. Then, the sensing capability is proven by burying the proposed sensor under sand and measuring the dc outputs of rectifiers. A strong correlation between simulation and measurement results validates the design procedure.

Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN)

Agriculture is closely associated with food and water. Agriculture is the first source of food but the biggest consumer of freshwater. The population is constantly increasing. Smart agriculture is one of the means of achieving food and water security. Smart agriculture can help improve water management and increase agricultural production, thus counteracting rapid population growth requirements. Soil moisture estimation is a critical step in agricultural water management. Soil moisture measurement techniques in situ are point measurements, labor-intensive, time-consuming, tedious, and expensive. We propose, in this research, a new approach to predict soil moisture over vegetation-covered areas from Sentinel-2 images based on a convolutional neural network (CNN). CNN architecture (3) consisting of six convolutional layers, one pooling layer, and two fully connected layers has achieved the highest prediction accuracy. Three well-known criteria including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) are utilized to measure the accuracy of the proposed algorithm. The Red Edge 3, NIR, and SWIR 1 are the most appropriate Sentinel-2 bands for retrieving soil moisture in vegetation-covered areas. Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) are the best indicators. The use of the indicator is more proper than the use of the single Sentinel-2 band as input data for the proposed CNN architecture for predicting soil moisture. However, using combinations “that consist of some number of Sentinel-2 bands” as input data for CNN architecture is better than using each indicator separately or all of them as a group. The best values of the performance metrics were achieved using the sixth combination (R2=0.7094, MAE=0.0277, RMSE=0.0418) composed of the Red, Red Edge 1, Red Edge 2, Red Edge 3, NIR, and Red Edge 4 bands as input data to the CNN architecture (3), as well as by using the fifth combination (R2=0.7015, MAE=0.0287, RMSE=0.0424) composed of the Red Edge 3, NIR, Red Edge 4, and SWIR 1 bands.

Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors.

Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of ~1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management.

Estimation of corn coefficients with vegetation indices using multispectral camera and drone

Optimum irrigation scheduling and new technologies are the key to the successful practice of modern agriculture and natural resources, such as water management. Α three-year research project was conducted at Velestino, Magnesia, Greece. The aim was to study whether vegetation indices can be used to estimate the crop coefficients of corn in order to apply an intelligent method of irrigation using drones in the future. The normalised difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), the renormalised difference vegetation index (RDVI) and a new index [difference infrared – green vegetation index (DIGVI)] were calculated using multispectral photos from a camera adapted to a drone. Three different methods were applied to calculate the crop coefficients: (i) the water balance and the FAO Penman-Monteith reference evapotranspiration, (ii) the climatic data, (iii) the vegetation indices. The irrigation dose covered 100% of the crop water needs according to the soil moisture measurements and the single crop coefficient values. The statistical analysis and the simple linear regression method showed that the corn crop coefficients can be estimated when these indices are used as independent variables.

Evaluating the suitability of a new telemetric capacitance-based measurement system for real-time application in irrigation and fertilization management

The suitability of a new wireless smart farming system for controlling irrigation and fertilization of horticultural plants was assessed in the study. The system (name: AGREUS®) includes sensors (soil moisture, salinity, weather data), executive modules (valve modules), and an application available on the web portal (accessed through computers and mobile devices). The studies were performed under laboratory and field conditions. Laboratory tests included appraisal of the precision of soil moisture and salinity measurements carried out with the soil probe (comparison with the results obtained by laboratory methods). Operational tests were conducted in field trials. In these trials, assessment of the possibility of practical control of irrigation and monitoring soil salinity was performed in an apple orchard. The conducted analyses have shown the usefulness of the system, not only for automatic control of irrigation but also for making decisions about the necessity to fertilize plants. The system enables continuous monitoring of changes in soil moisture and salinity, including the migration of minerals across the soil profile (using a probe with several measuring elements) as a result of the applied irrigation or rainfall. The system allows for automatic application of irrigation or fertigation depending on the adopted soil moisture and salinity thresholds. However, the tests showed that a salinity index calculated by the system does not directly correspond to the salinity values determined by laboratory methods. For this reason individual interpretation and determination of optimal ranges for plants is required.