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1. Online Soil Classification Using a UAS Sensor Emplacement System

   https://doi.org/10.1007/978303071151116  

   Plowcha, A.; Hogberg, J.; Detweiler, C.; Bradley, J (March 2021, International Symposium on Experimental Robotics)

   Siciliano, B.; Laschi, C.; Khatib, O. (Ed.)

   Deployment of sensors in hard-to-access locations can improve data gathering for scientific studies. We have developed a sensor emplacement system that can be mounted to unmanned aircraft systems with vertical takeoff and landing capabilities to autonomously auger a sensor into the ground. Various techniques can be chosen to enhance the augering process when certain characteristics of the soil are known. Moisture content and compressive strength are the soil characteristics that most impact the augering process, yet directly measuring them would require additional sensors to an already-burdened airframe. We address this through a novel means of predicting these soil characteristics within the first 30 s of an average 85 s augering evolution using onboard sensors and a Gaussian process regression scheme that predicts the soil moisture content and compressive strength with accuracy of 86.53% and 90.53% of the respective measured values. 

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2. Impact of Vetiver Plantation on Unsaturated Soil Behavior and Stability of Highway Slope

   https://doi.org/10.3390/geosciences14050123  

   Rahman, Fariha; Chakraborty, Avipriyo; Khan, Sadik; Salunke, Rakesh (May 2024, Geosciences)

   Due to cyclic wetting and drying, the hydro-mechanical behavior of unsaturated soil is impacted significantly. In order to assess the soil strength parameters, knowing the unsaturated behavior is important. Soil moisture content is an important parameter that can define the shear strength of the soil. Most of the highway slopes of Mississippi are built on highly expansive clay. During summer, the evaporation of moisture in the soil leads to shrinkage and the formation of desiccation cracks, while during rainfall, the soil swells due to the infiltration of water. In addition to this, the rainwater gets trapped in these cracks and creates perched conditions, leading to the increased moisture content and reduced shear strength of slope soil. The increased precipitation due to climate change is causing failure conditions on many highway slopes of Mississippi. Vetiver, a perennial grass, can be a transformative solution to reduce the highway slope failure challenges of highly plastic clay. The grass has deep and fibrous roots, which provide additional shear strength to the soil. The root can uptake a significant amount of water from the soil, keeping the moisture balance of the slope. The objective of the current study is to assess the changes in moisture contents of a highway slope in Mississippi after the Vetiver plantation. Monitoring equipment, such as rain gauges and moisture sensors, were installed to monitor the rainfall of the area and the moisture content of the soil. The data showed that the moisture content conditions were improved with the aging of the grass. The light detection and ranging (LiDAR) analysis was performed to validate the field data obtained from different sensors, and it was found that there was no significant slope movement after the Vetiver plantation. The study proves the performance of the Vetiver grass in improving the unsaturated soil behavior and stability of highway slopes built on highly expansive clay. 

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3. Probabilistic Analysis of In Situ Soil Water Characteristic Curve Using Kernel Density Estimation

   https://doi.org/10.1061/9780784485354.027  

   Alam, Md. Jobair; Aggarwal, Maalvika; Rahman, Naima (February 2024, American Society of Civil Engineers)

   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. 

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4. Classifying Soil Saturation Levels Using a Network of UAV-Deployed Smart Penetrometers

   https://doi.org/10.1115/smasis2023-111009  

   Chowdhury, Puja; Satme, Joud N; Yount, Ryan; Downey, Austin_R J; Khan, Sadik; Imran, Jasim; Micheli, Laura (September 2023, American Society of Mechanical Engineers)

   Abstract Levees are built to safeguard human lives, essential infrastructure, and farmland. However, failure of levees can have catastrophic impacts due to a fast rate of inundation in areas protected by levees. Earthen levees are prone to failure due to excessive moisture content that reduces the shear strength of the soil. The use of levee monitoring systems has demonstrated the ability to reduce the likelihood of failure by creating maps that depict the saturation levels of the surface of the levee, both in terms of space and time. By utilizing extensive sensor networks to continuously monitor these geo-infrastructure systems, the structural deterioration attributed to changing climate can be studied. Measuring environmental parameters surrounding such structures provides insight into the potential stressors that cause structural failure. Steps can then be taken to mitigate those effects on the levees and maintain structural integrity. However, the massive scale of levees makes it difficult to monitor with conventional wired sensors. This paper presents a preliminary investigation into the development and validation of UAV-deployable smart sensing spikes for soil conductivity levels in levees, which is a measurement modality for determining soil saturation levels. For this work, Gaussian process regression (also known as kriging) is used to model the soil saturation levels between sensing spikes obtaining a continuous moisture map of the levees. The expanded data is then categorized using a clustering-based machine learning approach with conductivity data from sensing spikes as model inputs. The machine learning model output is sorted into three categories: dry, partially saturated, and saturated soil. The findings of a laboratory study are presented, and the implications of the raw and expanded data are discussed. This work will aid in predicting potential levee failure risks and maintenance requirements based on the analysis of the soil conditions using a network of smart sensing spikes. 

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5. Nanofibrous mat-enabled soil water absorption for continuous soil nitrate sensing

   https://doi.org/10.1016/j.compag.2025.110319  

   Haridass, Vignesh Kumar; Castellano, Michael J; Dong, Liang (July 2025, Computers and Electronics in Agriculture)

   Continuous monitoring of soil nitrate levels is essential for effective soil nutrient management. However, limited soil pore water at low soil water content levels hinders miniaturized soil sensor surfaces from efficiently interacting with nutrient ions. To address this, we introduce a nanofibrous mat designed to enhance nitrate detection by increasing connectivity between miniature sensors and the soil solution. Composed of polysulfone, polymethylmethacrylate, and polyvinyl alcohol, this mat is fabricated using electrospinning and electrospray methods to balance water absorbency, mechanical durability, and ease of manufacturing. When wrapped around an ion-selective electrode-based nitrate sensor, the mat improves access to soil pore water, acts as a filter, prevents direct sensor-soil particle contact, and reduces the impact of soil particle surface charges on sensor measurements. Continuous nitrate monitoring with both mat-wrapped and bare sensors was conducted in controlled and field environments. Linear regression analysis indicates that the mat-wrapped sensor has a stronger correlation with conventional salt extract methods for measuring soil nitrate levels. T-tests confirm statistically significant differences between sensor measurements and the salt extraction method. Additionally, Bland-Altman analysis reveals that mat-wrapping reduces mean bias and narrows the limits of agreement, demonstrating improved agreement with the conventional method. Notably, the mat-wrapped sensor performs consistently across varying soil moisture conditions. These findings suggest that the water-absorbing mat improves the ability of the sensor to monitor nitrate continuously by accommodating varying soil moisture levels over time, making the mat-wrapped soil nitrate sensor a viable improvement for in-field measurements of soil solution chemistry. 

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