Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters.
more »
« less
Large Range Soil Moisture Sensing for Inhomogeneous Environments Using Magnetic Induction Networks
Water resource has become one of the most precious resources in recent decades. Agriculture accounts for about 80\% of the total water usage in US. There is a demanding need for efficient irrigation and water management systems built for sustainable water utilization in smart agriculture. Real time in-situ soil moisture sensing is a vital part for smart agriculture. Traditional electromagnetic (EM) based soil moisture sensing relies on EM based wireless sensor or ground penetrating radar (GPR) system. Based on the receiving signal strength and delay, tomographic techniques are used to derive the dielectric parameters of the soil, which are then into soil moisture distribution using empirical model. However, the EM signal attenuate sharply during underground propagation because of high operating frequency and lossy medium. In order to counter the disadvantage for underground sensing, we propose a Magnetic Induction (MI) based large range soil moisture sensing scheme in inhomogeneous environments. Here, we present the topology of the sensing system and analyze the channel model. The sensing process is based on transformed model, the conductivity and permittivity distribution are derived using SIRT algorithm. Through COMSOL simulation and analytical results, our proposed soil moisture sensing method achieves a root mean square error (RMSE) of 0.06 m^3/m^3 in 40 m 2D scale inhomogeneous environment range.
more »
« less
- Award ID(s):
- 1652502
- PAR ID:
- 10163050
- Date Published:
- Journal Name:
- 2019 IEEE Global Communications Conference (GLOBECOM)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Wide-area soil moisture sensing is a key element for smart irrigation systems. However, existing soil moisture sensing methods usually fail to achieve both satisfactory mobility and high moisture estimation accuracy. In this paper, we present the design and implementation of a novel soil moisture sensing system, named as SoilId, that combines a UAV and a COTS IR-UWB radar for wide-area soil moisture sensing without the need of burying any battery-powered in-ground device. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received radar signals, and automatically detect and discard the data contaminated by the UAV's uncontrollable motion and the multipath interference. Furthermore, we leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. We have extensively evaluated SoilId against a variety of real-world factors, including the UAV's uncontrollable motion, the multipath interference, soil surface coverages, and many others. Specifically, the experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.more » « less
-
Smart city transportation infrastructure will soon demand the development of reliable underground IoT (IoUT) communication. In this paper, we develop a novel analytical model, MAME (Material Aware Measurement Enhanced), to capture signal propagation properties in wireless IoUT networks to achieve reliable data transport. A driving motivation is monitoring underground infrastructure systems (e.g., pipelines and storm drains) for early detection of anomalies and failures to guide human investigation and intervention. We analyze the feasibility of successfully receiving wireless data packets from underground (UG) sensor nodes through multiple material layers and under diverse environmental conditions. Our proposed approach integrates physics-based modeling and empirical studies with small-scale testbeds (in our lab and outdoors) with multiple channel setups and physical layer attributes. We derive a novel MAME approach to model signal propagation in both 802.11-based WiFi and LoRaWAN networks. The resulting MAME model is shown to capture communication behavior in WiFi and LoRaWAN networks accurately. The MAME model is used to augment the popular NS3 simulator to explore scaled-up underground networks and varying channel conditions (e.g., soil moisture level). Such a combined analytical-empirical approach will enable the communication control plane and application layer to better predict channel conditions for improved IoUT network design.more » « less
-
Abstract Soil sensors and plant wearables play a critical role in smart and precision agriculture via monitoring real‐time physical and chemical signals in the soil, such as temperature, moisture, pH, and pollutants and providing key information to optimize crop growth circumstances, fight against biotic and abiotic stresses, and enhance crop yields. Herein, the recent advances of the important soil sensors in agricultural applications, including temperature sensors, moisture sensors, organic matter compounds sensors, pH sensors, insect/pest sensors, and soil pollutant sensors are reviewed. Major sensing technologies, designs, performance, and pros and cons of each sensor category are highlighted. Emerging technologies such as plant wearables and wireless sensor networks are also discussed in terms of their applications in precision agriculture. The research directions and challenges of soil sensors and intelligent agriculture are finally presented.more » « less
-
T. Matthew Evans, Ph.D. Nina Stark (Ed.)Soil water characteristic curve (SWCC) which describes the relationship between water content and matric suction is important to analyzing unsaturated soil behavior. Because of the degree of uncertainty in field conditions due to climatic variability and soil heterogeneities, it becomes necessary to probabilistically characterize the SWCC. A satisfactory probabilistic characterization of field-based SWCCs requires a substantial data pair of water content and suction and their distribution characteristics. In this study, the kernel density estimate (KDE) approach was applied to water content and suction data measured from field-installed co-located sensors of a compacted clay bed to (1) determine the modality of water content and suction distribution and their constitutive relationship at variable weather conditions and (2) demonstrate the importance of probabilistic analysis of SWCC. The Gaussian function was used in the KDE analysis. A moisture sensor and soil water potential sensor were juxtaposed at 0.3 m depth of the 3 m × 3 m compacted clay bed to collect the water content and suction data and determine their distribution under the field condition. The density plots of both water content and suction at 0.3 m depth exhibited multimodal distribution due to the uneven distribution of climatic events. The KDE reasonably identified the air entry value, saturated moisture content, and residual moisture content in the field conditions, which were validated with field-based SWCC plots. The study showed that probabilistic analysis better interprets the realistic scenarios of field unsaturated soil behavior.more » « less
An official website of the United States government

