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Soil moisture monitoring is essential for agriculture and environmental management, yet existing methods require either invasive probes disturbing the soil or specialized equipment, limiting access to the public. We present SoilSound, an ubiquitous accessible smartphone-based acoustic sensing system that can measure soil moisture without disturbing the soil. We leverage the built-in speaker and microphone to perform a vertical scan mechanism to accurately measure moisture without any calibration. Unlike existing work that use transmissive properties, we propose an alternate model for acoustic reflections in soil based on the surface roughness effect to enable moisture sensing without disturbing the soil. The system works by sending acoustic chirps towards the soil and recording the reflections during a vertical scan, which are then processed and fed to a convolutional neural network for on-device soil moisture estimation with negligible computational, memory, or power overhead. We evaluated the system by training with curated soils in boxes in the lab and testing in the outdoor fields and show that SoilSound achieves a mean absolute error (MAE) of 2.39% across 10 different location

The complex background in the soil image collected in the field natural environment will affect the subsequent soil image recognition based on machine vision. Segmenting the soil center area from the soil image can eliminate the influence of the complex background, which is an important preprocessing work for subsequent soil image recognition. For the first time, the deep learning method was applied to soil image segmentation, and the Mask R-CNN model was selected to complete the positioning and segmentation of soil images. Construct a soil image dataset based on the collected soil images, use the EISeg annotation tool to mark the soil area as soil, and save the annotation information; train the Mask R-CNN soil image instance segmentation model. The trained model can obtain accurate segmentation results for soil images, and can show good performance on soil images collected in different environments; the trained instance segmentation model has a loss value of 0.1999 in the training set, and the mAP of the validation set segmentation (IoU=0.5) is 0.8804, and it takes only 0.06s to complete image segmentation based on GPU acceleration, which can meet the real-time segmentation and de

Soil degradation threatens agricultural productivity and food supply, leading to hunger issues in some developing regions. To address this challenge, we developed a low-cost, highly efficient, and long-term stable soil improvement method. We chose polyvinyl alcohol (PVA), a commercially available polymer that is safe and non-degradable, to serve as a soil adhesive. We mixed PVA solution into the soil and applied a drying treatment to enhance the bonding between PVA and the soil, achieving highly water-stable soil. This PVA-stabilized soil exhibits low bulk density, high porosity, and high permeability, making it an ideal substrate for planting. In a germination test, the PVA-stabilized soil revealed a higher germination rate and growth rate compared to those of the non-treated soil. We believe this simple and efficient soil improvement method can restore degraded soil and contribute to sustainable agriculture.

Hazardous persistent organic pollutants (POPs) interact in soil with the soil organic matter (SOM) but this interaction is insufficiently understood at the molecular level. We investigated the adsorption of hexachlorobenzene (HCB) on soil samples with systematically modified SOM. These samples included the original soil, the soil modified by adding a hot water extract (HWE) fraction (soil+3 HWE and soil+6 HWE), and the pyrolyzed soil. The SOM contents increased in the order pyrolyzed soil < original soil < soil+3 HWE < soil+6 HWE. For the latter three samples this order was also valid for the HCB adsorption. The pyrolyzed soil adsorbed more HCB than the other samples at low initial concentrations, but at higher concentrations the HCB adsorption became weaker than in the samples with HWE addition. This adsorption behaviour combined with the differences in the chemical composition between the soil samples suggested that alkylated aromatic, phenol, and lignin monomer compounds contributed most to the HCB adsorption. To obtain a molecular level understanding, a test set has been developed on the basis of elemental analysis which comprises 32 representative soil constituents. T

Development of a spatial-temporal and data-driven model of soil respiration at the global scale based on soil temperature, yearly soil moisture, and soil organic carbon (C) estimates. Prediction of soil respiration on an annual basis (1991-2018) with relatively high accuracy (NSE 0.69, CCC 0.82). Lower soil respiration trends, higher soil respiration magnitudes, and higher soil organic C stocks across areas experiencing the presence of sustainable soil management practices.

Soil sensing plays an important role in increasing agricultural output and protecting soil sites. Existing soil sensing methods failed to achieve both high accuracy and low cost. In this paper, we design and implement a high-accuracy and low cost chipless soil moisture sensing system called SoilTAG. We propose a general chipless sensor design methodology which can allow us to customize the signal feature for sensing soil moisture, instead of blindly capturing the disturbance law of the soil. Based on this principle, we design a battery-free passive tag which can respond to different soil-moisture. Further, we optimize hardware and algorithm design of SoilTAG to locate the passive tag and extract its reflection signal feature to identify soil-moisture using WiFi signals. Extensive experimental results reveal that it can identify 2% absolute soil water content with a sensing distance up to 3m in open field. When the sensing distance is up to 13 m, it can also achieve 5% absolute soil-moisture sensing resolution.

PCR-based analysis of DNA is utilized in a wide variety of fields, including Forensic Science. Aside from the more common ample sources, material analyzed here can refer to specimen excavated from a soil environment, or a sampling of the soil itself to recover DNA leached into the soil from decomposing human remains or from body fluids intermingled with the soil in an outdoor crime scene. The common problematic of these types of sample is the presence of humic acids, which are a component of any soil environment, and when the co-extracted with the DNA, lead to inhibition of enzyme-based procedures including PCR. While a variety of methods exist for the extraction of DNA from excavated skeletal remains, protocols for extraction of DNA from the soil directly are usually targeting soil microorganism. To address the need for methodology suitable for extraction of human DNA from soil, a selection of three published protocols were adapted for this purpose, to be tested and evaluated using standardized samples. The resulting protocols are presented here.

Soil moisture estimation is an important task to enable precision agriculture in creating optimal plans for irrigation, fertilization, and harvest. It is common to utilize statistical and machine learning models to estimate soil moisture from traditional data sources such as weather forecasts, soil properties, and crop properties. However, there is a growing interest in utilizing aerial and geospatial imagery to estimate soil moisture. Although these images capture high-resolution crop details, they are expensive to curate and challenging to interpret. Imagine, an AI-enhanced software tool that predicts soil moisture using visual cues captured by smartphones and statistical data given by weather forecasts. This work is a first step towards that goal of developing a multi-modal approach for soil moisture estimation. In particular, we curate a dataset consisting of real-world images taken from ground stations and their corresponding weather data. We also propose MIS-ME - Meteorological & Image based Soil Moisture Estimator, a multi-modal framework for soil moisture estimation. Our extensive analysis shows that MIS-ME achieves a MAPE of 10.14%, outperforming traditional unimodal a

An improved understanding of soil can enable more sustainable land-use practices. Nevertheless, soil is called a complex, living medium due to the complex interaction of different soil processes that limit our understanding of soil. Process-based models and analyzing observed data provide two avenues for improving our understanding of soil processes. Collecting observed data is cost-prohibitive but reflects real-world behavior, while process-based models can be used to generate ample synthetic data which may not be representative of reality. We propose a framework, knowledge-guided representation learning, and causal structure learning (KGRCL), to accelerate scientific discoveries in soil science. The framework improves representation learning for simulated soil processes via conditional distribution matching with observed soil processes. Simultaneously, the framework leverages both observed and simulated data to learn a causal structure among the soil processes. The learned causal graph is more representative of ground truth than other graphs generated from other causal discovery methods. Furthermore, the learned causal graph is leveraged in a supervised learning setup to predict

Soil quality (SQ) plays a crucial role in sustainable agriculture, environmental conservation, and land-use planning. Traditional SQ assessment techniques rely on costly, labor-intensive sampling and laboratory analysis, limiting their spatial and temporal coverage. Advances in Geographic Information Systems (GIS), remote sensing, and machine learning (ML) enabled efficient SQ evaluation. This paper presents a comprehensive roadmap distinguishing it from previous reviews by proposing a unified and modular pipeline that integrates multi-source soil data, GIS and remote sensing tools, and machine learning techniques to support transparent and scalable soil quality assessment. It also includes practical applications. Contrary to existing studies that predominantly target isolated soil parameters or specific modeling methodologies, this approach consolidates recent advancements in Geographic Information Systems (GIS), remote sensing technologies, and machine learning algorithms within the entire soil quality assessment pipeline. It also addresses existing challenges and limitations while exploring future developments and emerging trends in the field that can deliver the next generation

This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment, with a focus on key indicators such as available boron (B), organic carbon (OC), available manganese (Mn), available sulfur (S), and the sulfur availability index (SAI). A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed. The research integrated color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results showed that combining image features (IFs) with AVs significantly improved prediction accuracy for available B (R2 = 0.80) and OC (R2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further enhanced predictions for available Mn and SAI, with R2 values of 0.72 and 0.70, respectively. The study highlights the potential of integrating these technologies to offer rapid, cost-effective soil testing methods, paving the way for more advanced predictive models and a deeper understanding of soil fertility. Future work should explore the application of deep learning models on a larger dataset, incorporatin

In the field of pedometrics, tabular machine learning is the predominant method for soil property prediction from remote and proximal soil sensing data, forming a central component of Digital Soil Mapping (DSM). At the field-scale, this predictive soil modeling (PSM) task is typically constrained by small training sample sizes and high feature-to-sample ratios in soil spectroscopy. Traditionally, these conditions have proven challenging for conventional deep learning methods. Classical machine learning algorithms, particularly tree-based models like Random Forest and linear models such as Partial Least Squares Regression, have long been the default choice for pedometric modeling within DSM. Recent advances in artificial neural networks (ANN) for tabular data challenge this view, yet their suitability for field-scale DSM has not been proven. We introduce a comprehensive benchmark that evaluates state-of-the-art ANN architectures, including the latest multilayer perceptron (MLP)-based models (TabM, RealMLP), attention-based transformer variants (FT-Transformer, ExcelFormer, T2G-Former, AMFormer), retrieval-augmented approaches (TabR, ModernNCA), and an in-context learning foundation

Environmental variables are increasingly affecting agricultural decision-making, yet accessible and scalable tools for soil assessment remain limited. This study presents a robust and scalable modeling system for estimating soil properties in croplands, including soil organic carbon (SOC), total nitrogen (N), available phosphorus (P), exchangeable potassium (K), and pH, using remote sensing data and environmental covariates. The system employs a hybrid modeling approach, combining the indirect methods of modeling soil through proxies and drivers with direct spectral modeling. We extend current approaches by using interpretable physics-informed covariates derived from radiative transfer models (RTMs) and complex, nonlinear embeddings from a foundation model. We validate the system on a harmonized dataset that covers Europes cropland soils across diverse pedoclimatic zones. Evaluation is conducted under a robust validation framework that enforces strict spatial blocking, stratified splits, and statistically distinct train-test sets, which deliberately make the evaluation harder and produce more realistic error estimates for unseen regions. The models achieved their highest accuracy f

Soil is a critical component of terrestrial ecosystems, directly influencing global biogeochemical cycles. Despite its importance, the complex architecture of soil pores and their impact on greenhouse gas emissions remain poorly understood. This perspective aims to address this gap by applying discrete symmetry and symmetry-breaking concepts through fractal geometry to elucidate the structural and functional complexities of soil pores. We highlight how fractal parameters can quantify the self-similar nature of soil pore structures, revealing their size, shape, and connectivity. These geometric attributes influence soil properties such as permeability and diffusivity, which are essential for understanding gas exchange and microbial activity within the soil matrix. Furthermore, we emphasize the effects of various land management practices, including tillage and wetting-drying cycles, on soil pore complexity using three-dimensional multi-fractal analysis. Literature indicates that different agricultural practices significantly alter pore heterogeneity and connectivity, affecting greenhouse gas emissions. Conventional tillage decreases pore connectivity and increases randomness, wherea

The research assesses soil salinity in the southwest coastal region of Bangladesh, collecting a total of 162 topsoil samples between March 1 and March 9, 2024, and processing them following the standard operating procedure for soil electrical conductivity (soil/water, 1:5). Electrical conductivity (EC) measurements obtained using a HI-6321 advanced conductivity benchtop meter were analyzed and visualized using bubble density mapping and the Empirical Bayesian Kriging interpolation method. The findings indicate that soil salinity in the study area ranges from 0.05 to 9.09 mS/cm, with the highest levels observed near Debhata and Koyra. A gradient of increasing soil salinity is clearly evident from the northern to southern regions. This dataset provides a critical resource for soil salinity-related research in the region, offering valuable insights to support decision-makers in understanding and mitigating the impacts of soil salinity in Bangladesh's coastal areas.

Soil physics models have long relied on simplifying assumptions to represent complex processes, yet such assumptions can strongly bias model predictions. Here, we propose a paradigm-shifting differentiable hybrid modeling (DHM) framework that instead of simplifying the unknown, learns it from data. As a proof of concept, we apply the hybrid approach to the challenge of partitioning the soil water retention curve (SWRC) into capillary and adsorbed water components, a problem where traditional assumptions have led to divergent results. The hybrid framework derives this partitioning directly from data while remaining guided by a few parsimonious and universally accepted physical constraints. Using basic soil physical properties as inputs, the hybrid model couples an analytical formula for the dry end of the SWRC with data-driven physics-informed neural networks that learn the wet end, the transition between the two ends, and key soil-specific parameters. The model was trained on a SWRC dataset from 482 undisturbed soil samples from Central Europe, spanning a broad range of soil texture classes and organic carbon contents. The hybrid model successfully learned both the overall shape an

Agricultural production heavily exploits the soil, resulting in high erosion in cultivated land, which poses a threat to food security and environmental sustainability. To address this issue, we stabilize the soil using polyvinyl alcohol (PVA). PVA strongly adheres to the soil after mixing and annealing, enhancing the cohesive strength of the soil. The PVA-soil withstands the impact of water at 7 m/s, protecting it from rainfall-induced erosion. Furthermore, the water-retaining capability and drainage of PVA-soil can be adjusted based on its sizes. This customized PVA-soil provides optimal growing conditions for various plants in different climates. Our method contributes to improved soil management and conversion.

The loss of soil organic carbon (SOC) poses a severe danger to agricultural sustainability around the World. This review examines various farming practices and their impact on soil organic carbon storage. After a careful review of the literature, most of the research indicated that different farming practices, such as organic farming, cover crops, conservation tillage, and agroforestry, play vital roles in increasing the SOC content of the soil sustainably. Root exudation from cover crops increases microbial activity and helps break down complex organic compounds into organic carbon. Conservation tillage enhances the soil structure and maintains carbon storage without disturbing the soil. Agroforestry systems boost organic carbon input and fasten nutrient cycling because the trees and crops have symbiotic relationships. Intercropping and crop rotations have a role in changing the composition of plant residues and promoting carbon storage. There were many understanding on the complex interactions between soil organic carbon dynamics and agricultural practices. Based on the study, the paper reveals, the role of different agricultural practices like Carbon storage through cover crops,

Established in 2012 by members of the Food and Agriculture Organisation (FAO), the Global Soil Partnership (GSP) is a global network of stakeholders promoting sound land and soil management practices towards a sustainable world food system. However, soil survey largely remains a local or regional activity, bound to heterogeneous methods and conventions. Recognising the relevance of global and trans-national policies towards sustainable land management practices, the GSP elected data harmonisation and exchange as one of its key lines of action. Building upon international standards and previous work towards a global soil data ontology, an improved domain model was eventually developed within the GSP [54], the basis for a Global Soil Information System (GloSIS). This work also identified the Semantic Web as a possible avenue to operationalise the domain model. This article presents the GloSIS web ontology, an implementation of the GloSIS domain model with the Web Ontology Language (OWL). Thoroughly employing a host of Semantic Web standards (SOSA, SKOS, GeoSPARQL, QUDT), GloSIS lays out not only a soil data ontology but also an extensive set of ready-to-use code-lists for soil descri