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This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of soil based on features extracted from in situ soil surface images. Soil surface images were acquired using a Canon EOS 100d camera. The camera was installed in the vertical direction above the soil surface layer. To maintain uniform illumination conditions, a dark room and LED lighting were utilized. Following the acquisition of soil surface images, soil samples were collected using a metal cylinder to obtain measurements of soil water content and bulk density. Various features were extracted from the images, including color, texture, and shape features, and used as inputs for both a multiple regression analysis and a deep neural network model. The results show that the deep neural network regression model can predict soil water content and bulk density with root mean squared error of 1.52% and 0.78 kN/m3. The deep neural network model outperformed the multiple regression analysis, achieving a high accuracy for predicting both soil water content and bulk density. These findings suggest that in situ soil surface images, combined with deep learning techniques, can provide a fast and reliable method for predicting important soil properties.

It is of great significance to obtain soil texture information quickly for the realization of farmland management. Soil with good particle condition can well regulate the needs of plants for water, nutrients, air, and temperature during crop growth, thereby promoting high crop yields. The existing methods of measuring soil texture cannot meet the requirements of time and spatial resolution. For this reason, a vehicle-mounted soil texture detector was designed and developed based on machine vision and soil electrical conductivity devices. The detector does not require pretreatment such as air-drying and screening of the soil, and completely uses the original information of the farmland. The whole process can obtain the soil texture information in real time, omitting the complicated chemical process, and saving manpower and material resources. The vehicle-mounted detector is divided into a mechanical part, a control part, and a display part. The mechanical part provides measurement support for the acquisition of soil texture information; the control part collects and processes signals and images; the measurement results can be intuitively observed and recorded on the display, and can be operated through the mobile phone. The vehicle-mounted detector obtains soil conductivity through 4 disc electrodes, while the vehicle-mounted industrial camera captures the soil surface image, and extracts texture parameters through image processing, takes EC and texture parameters as input, and the embedded SVM model of the instrument was used to perform soil texture prediction. In order to verify the measurement accuracy of the detector, farmland verification experiments were carried out on farmland loam in Tongzhou District and Haidian District of Beijing. The R2 of the correlation between the measured value of soil EC and the actual value was 0.75, and the accuracy of soil texture prediction was 84.86%. It shows that the developed vehicle-mounted soil texture detector can meet the requirements for rapid acquisition of farmland texture information.

Soil texture is one of the most important soil eharaeteristies that affect soil properties. Rapid acquisition of soil texture information is of great significance for accurate farmland management. Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution. This research introduced a self-developed vehicle-mounted in-situ soil texture detection system, which can predict the type of soil texture and the particle composition of the texture, and obtain real-time data during the measurement process without preprocessing the soil samples. The detection system is mainly composed of a conductivity measuring device, a camera, an auxiliary mechanical structure, and a control system. The soil electrical conductivity (ECa) and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis. In order to find the best model suitable for the detection system, measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system. The results showed that for soil texture classification, ExtraTrees performed best, with Precision, Recall, and F1 all being 0.82. For particle content of soil texture prediction, the R2 of ExtraTrees was 0.77, and RMSE and MAPE were 74.72 and 39.58. It was observed that ECa, Moment of inertia, and Entropy had larger weights in the drawn model influence weight map, and they are the main contributors to predicting soil texture. These results showed the potential of the vehicle-mounted in-situ soil texture detection system, which can provide a basis for fast, cost-effective, and efficient soil texture analysis.

PurposeIn the pile-soil interaction system, the disturbed soil directly affects the safety of the laterally loaded pile. The soil displacement field helps to evaluate the range and degree of soil disturbance. This study presents a method of visualiziing the displacement field of the soil around the laterally loaded pile by using transparent soil technology, which overcomes the measurement obstacles caused by the non-transparency of the real soil.MethodsGlass sand and transparent pore solution were mixed to make a saturated transparent soil with two particle sizes (0.1 ~ 0.5 mm and 0.5 ~ 1 mm). Instead of real soil, transparent soil was used to observe the degree of disturbance in the process of interaction with laterally loaded piles. In addition, particle image velocimetry (PIV) was used to capture the displacement of transparent soil particles. The displacement of each particle was integrated into the displacement field by a MATLAB program.ResultsWhen a horizontal force was applied on the top of the pile, the particles in front of the pile were compressed, producing observable movement within a certain area. From the displacement vector diagram, it could be seen that the displacement area of the soil surface in front of the pile increases as the layer thickness of large particle soil increases. The vertical displacement of soil in front of the pile was compacted to form a wedge-shaped area under the horizontal load. The angle between the direction of soil motion and the horizontal plane was positively correlated with the thickness of the soil layer.ConclusionTransparent soil and particle image velocimetry can help reveal the displacement trends of the soil around a laterally loaded pile. Based on this, an early warning can be provided when the displacement value and displacement angle of the soil around the laterally loaded pile exceeds the normal range.

PurposePreferential flow via soil macropores can have a large effect on water quality. Hence, it is important to quantify soil macropore characteristics to better understand preferential flow behavior in soils. Currently, little information exists on the changes in soil macroporosity in response to topographical position within a field and how macropore characteristics change temporally. The objective of this study was to use X-ray computed tomography (CT) and image analysis to quantify temporal and spatial variability in 3D soil macropore structure in a 0.40 ha pasture field.MethodsA total of 36 undisturbed soil columns, 150 mm in diameter and 500 mm in length, were collected during May and September of 2019 from a pasture field located in Alabama, USA. The image analysis was performed to quantify spatial and temporal variability in soil macropore characteristics.Results and discussionThe macropore characteristics varied significantly between different topographical positions and sampling seasons, especially at the surface layer (0–100 mm) depth. The soil macropores at the downslope position were sparsely distributed in the surface soil layer. This was attributed to a relatively higher degree of grazing-induced compaction due to higher soil moisture as compared to the upslope and midslope locations. In contrast, dense macropore networks were observed at the downslope positions for depths greater than 250 mm.ConclusionsThe results of this study show that macropore characteristics varied as a function of topography and time. Except macropore diameter, all other macropore characteristics showed an increasing trend from season 1 (spring) to season 2 (fall). The regeneration of macropores was mainly attributed to the wetting and drying cycles that promoted formation of smaller macropores (0.70–1 mm) at the surface soil, thereby reducing the average macropore diameter. Significant differences in the macropore characteristics were observed mostly in the surface layer (0–100 mm).

The present research seeks to comprehend and evaluate the impact of varying concentrations of HCl acid solution on the compressibility and shear strength of silty clay. The compression test and the unconfined compressive strength test were conducted, and the SEM test was performed to analyze the microstructure of the soil with and without contamination. The results indicate that the contamination of silty clay soil with HCl acid caused a reduction in strength and an increase in the compression index and coefficient of consolidation; the more significant change in compressibility and strength was seen when the acid solution became more acidic. Concerning the SEM test, the images demonstrate the formation of macro pores between soil particles due to soil contamination. As the HCl acid solution concentration increased, more pores were formed and irregularly distributed across the whole soil surface. HCl acid solution contamination of soil causes the soil characteristics to degrade generally. Construction on this soil would consequently need to take into account the environment. Preparing the soil before building on it is suggested by adding materials that can increase the acid resistance of the soil.

Purpose Cover crops have been used as an effective soil management practice to enhance soil health. However, this practice may create connected soil pore networks that can cause preferential transport of contaminants to the groundwater or surface water via subsurface flow pathways. The main objective of this study was to compare the effect of cover crops on the soil macropore characteristics in the soil profile. Methods The study was conducted on soil columns collected from E.V. Smith Research Center, Shorter, AL, USA. This study evaluated the influence of cover crop (CC) vs. no cover crop (NC) on soil pore characteristics in strip-tillage cotton ( Gossypium hirsutum L.). The cover crop treatment consisted of a mixture of cereal rye ( Secale cereale L.) and crimson clover ( Trifolium incarnatum L.). Six replicated intact undisturbed soil cores (150 mm diameter and 500 mm deep) were collected for the column study from each treatment class, i.e., CC and NC, and subjected to non-invasive X-ray computed tomography (CT) scanning, giving 0.35-mm-resolution images. The high-resolution images were analyzed in ImageJ to determine all the soil pore characteristics. Results and discussion The results of the comparison of pore characteristics as a function of treatments showed that soil columns under CC had comparatively higher values of porosity and pore number density for the top 100 mm of soil. Pore geometry metrics such as tortuosity did not show significant differences among the treatments (CC vs NC). Connection probability was significantly higher for CC in the subsurface depth class (200–400 mm). Significant correlations were also observed between CT-derived pore characteristics and root characteristics from which it can be inferred that cover crop roots influenced the X-ray CT-derived pore properties. Conclusions Cover cropping significantly impacted the macropore properties of the strip-till cotton field. This was attributed primarily to the influence of root networks on macropores. Our study’s correlations between root properties and macropore characteristics also indicated that larger root volumes were significantly correlated with complex and irregularly shaped macropores. These variables are critical for a better understanding of the flow dynamics of contaminants through the soil profile and for developing appropriate management strategies.

Soil degradation is one of the main environmental issues within the international agendas on sustainability and climate adaptation. Among degradation processes, soil sealing represents the major threat, as ecosystem services dramatically decrease or are even nullified. The increasing use of big open data from satellites combined with AI algorithms are making geodata mining and mapping techniques essential to quantify soil sealing. Different keywords are adopted to define the phenomenon. However, at present, review articles presenting the state-of-the-art on mapping soil sealing by including the most common definitions are currently not available. Hence, we analyzed: (a) impervious surface, (b) soil sealing, (c) land take, (d) soil consumption, (e) land consumption. We provide a systematic review of remote sensing platforms and methodologies to map and to classify soil sealing, by highlighting: (a) definitions; (b) relationships among study areas, scales, platforms, resolutions, and classification methodologies; (c) emerging trends and policy implications. We performed a systematic search on Scopus (from 2000 to 2020), identifying 1277 papers; 392 focused on mapping soil sealing. ‘Impervious surface’ is the dominant definition. The phenomenon is more studied by the USA, China and Italy and, ‘soil sealing’ is recently more adopted in EU. Most studies focuses on mapping soil sealing at urban scale. We found Landsat are the most adopted platforms; they are frequently used for multi-temporal analyses. Eleven methodologies were identified: automatic classifications are the most adopted, dominated by pixel/sub-pixel-based approaches; other methods include Band Ratios, Supervised, OBIA, ANN. The majority of mapping analyses are performed on 30 m resolution in areas of 1000–10 000 km 2 . Landsat images are less used for smaller areas. In conclusion, as study area size increases, a decrease in image resolution with the use of more completely automatic classification methodologies is recorded. However, most studies focuses on comparing classification techniques rather than supporting policy making for sustainable urban planning. Thus, we encourage to fill the gap by developing approaches that applicable to international policies.

Landslides are common geological hazards that cause significant losses. Anti-slide piles are commonly used in landslide engineering, and model testing is one of the means to study pile-supported structures. However, model tests face several challenges, including difficulty in controlling the experimental process, challenges in repeated tests, and difficulty in monitoring soil deformation around piles. To address these issues, this study presents a model test method using particle image velocimetry (PIV), transparent soil, and 3D printing technology. Using this method, a series of model tests were conducted, including single-row and double-row anti-slide piles. The experimental results indicate that, compared with single-row piles, double-row piles exhibit better supporting effects. In the pile‒soil interaction, the displacement of the extrusion of soil between piles was controlled under the combined action of the front and back rows of piles. The inclination angle of a single-row pile after the test was 8°, whereas that of a double-row pile was reduced by 62.5%. With respect to the displacement of the soil behind the piles, the phenomenon of a “displacement triangle” behind the piles was observed. An analysis of the change process in this area revealed that the relative displacement caused by pile‒soil interactions is mainly distributed in the surface layer of the soil. The experiments demonstrate that this system is suitable for pile-supported structure model tests.