Search Results (779)

Machine learning (ML) is increasingly applied to geochemical baseline estimation and anomaly detection in soils and sediments, yet the methodological conditions under which machine learning outperforms traditional approaches—and which preprocessing and validation decisions most consequentially determine that advantage—remain incompletely characterized across environmental and mineral exploration domains. A structured systematic scoping review of 146 records from the Web of Science Core Collection applied sequential filtering to yield 78 thematically eligible studies, from which 20 were prioritized through a composite index integrating age-adjusted citation impact, platform usage, and semantic relevance. Four cross-cutting findings emerge. First, performance gains in environmental applications were driven primarily by spatial model structure rather than algorithm selection: incorporating a spatial covariate derived from geographically weighted regression raised test-set explained variance from $R^2=0.80$ to $R^2=0.96$ for cadmium mobility prediction in a geochemically heterogeneous karst setting, a gain the source study supported with a held-out test set and a Monte Carlo analysis of sensitivity to data size. Second, isometric or centered log-ratio preprocessing was applied in the majority of mineral exploration studies (three of five classical and hybrid studies and four of five deep-learning studies) but in none of the seven environmental studies, representing a systematic methodological gap with direct consequences for covariate importance estimates under compositional closure. Third, Shapley additive explanations and accumulated local effects functioned as instruments of operational value, enabling element-specific anomaly threshold derivation, training sample diagnosis, and grid-cell anomaly type classification; this evidence demonstrates that the accuracy–interpretability trade-off commonly assumed in the machine learning literature is not fundamental in geochemical applications but contingent on algorithm selection. Fourth, 90% of the 20 synthesized studies (18 of 20 by study-area location—13 in China and five in Iran) were evaluated under within-domain validation designs, and the consistently high performance metrics reported should be interpreted as interpolation estimates rather than evidence of transferable predictive capability. Geographic diversification of training datasets and spatially explicit cross-regional validation are identified as structural prerequisites for regulatory-grade applicability. Full article

Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), and remote sensing technologies have transformed agricultural monitoring, precision farming, and climate-resilient decision-making. However, the widespread adoption of AI-driven agricultural systems remains constrained by the black-box nature of advanced predictive models, particularly deep neural networks. Explainable Artificial Intelligence (XAI) has emerged as a critical solution for improving transparency, interpretability, accountability, and trust in AI-based agricultural remote sensing systems. This review provides a comprehensive synthesis of the recent developments in XAI applications within smart agriculture, with emphasis on interpretable remote sensing analytics and sustainable decision-making. The review discusses the evolution of AI in agriculture, major remote sensing platforms, explainability frameworks, and the integration of XAI with satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and geospatial big data. Key agricultural applications, including crop classification, yield prediction, disease detection, soil property assessment, irrigation management, carbon monitoring, and climate adaptation, are critically evaluated. Furthermore, the review compares intrinsic and post hoc explainability methods such as attention mechanisms, saliency maps, and counterfactual explanations. The interpretation of model outputs and reported results from recent studies is discussed to demonstrate how XAI improves model reliability and stakeholder confidence. Challenges related to data heterogeneity, scalability, uncertainty, ethics, fairness, and computational complexity are also analyzed. Finally, future perspectives are presented regarding hybrid explainable frameworks, physics-informed AI, edge computing, digital twins, and trustworthy autonomous agricultural systems. The review emphasizes the central role of XAI in enabling transparent and sustainable agricultural intelligence under rapidly changing climatic and environmental conditions. Full article

The Tarim Basin, as the core strategic replacement area for oil and gas resources in western China, holds immense deep oil and gas development potential. However, this region features extensive deserts, fragile saline–alkali ecosystems, and extreme geological conditions—160°C high temperatures, high-salt/high-alkali formation fluids, and well-developed microfractures—that impose stringent dual requirements on drilling fluids for both engineering performance and environmental compatibility. Traditional drilling fluids suffer severe performance deterioration under high-temperature/high-salt coupling, and their poorly biodegradable, toxic additives exacerbate ecological pollution upon disposal, failing to meet green development demands. To address these issues, this study prepared four eco-friendly, temperature- and salt-resistant drilling fluid additives via chemical modification of natural degradable materials, and constructed a 160°C-resistant eco-friendly water-based drilling fluid system using orthogonal experiments. Furthermore, an innovative “waste-to-resource” strategy was proposed to repurpose spent drilling fluid for saline–alkali land restoration, investigating its improvement effects and environmental safety. The results show that the system withstands 160 °C and 15% NaCl, with excellent engineering performance (HTHP filtration loss ≤ 9.0 mL at 160 °C, friction coefficient ≤ 0.18) and compliance with national environmental standards (EC₅₀ = 36,000 mg/L, BOD₅/COD = 6.8%). Applied at an optimal 10–15% dosage, the spent fluid significantly improves saline–alkali soil properties, promotes Haloxylon ammodendron growth, and achieves >90% pollutant degradation within 3 months without secondary pollution. This integrated technology synergizes drilling engineering and ecological protection, providing technical support for green oil and gas development and saline–alkali land restoration in the Tarim Basin. Full article

Beauveria bassiana (Balsamo-Crivelli) Vuillemin can colonize plant tissues as an endophyte, promoting plant growth and defense against pathogens and insect herbivory. Understanding the endophytic behavior of native isolates is important for developing pest management strategies in maize, a crop affected by Spodoptera frugiperda (J. E. Smith). This study aimed to genetically characterize a Mexican B. bassiana isolate (Bb-IIAF1-24) collected in Los Reyes, Michoacán, using the β-tubulin gene. The phylogenetic relationships showed that this isolate formed a well-supported clade with other B. bassiana isolates from different countries; however, a low divergence among them was observed. In a second part of this study, the influence of foliar and soil inoculation (1 × 10⁸ conidia mL⁻¹) of Bb-IIAF1-24 isolate on endophytic colonization in maize plants, as well as the effects on plant growth and herbivory by S. frugiperda were evaluated. This fungus was detected in roots, stems, and leaves, but no significant differences were found between root and stem colonization or between foliar and soil inoculation methods. Beauveria bassiana treatment resulted in increased stem diameter in plants when applied to soil compared to foliar application and the control. In contrast, plants subjected to foliar application were significantly taller than those receiving soil application or the control plants. Plants from both application methods experienced lower leaf damage compared to the control. These findings demonstrate the potential of the Bb-IIAF1-24 isolate as an endophytic fungus to promote maize growth and reduce herbivory by S. frugiperda. Full article

Reliance on mineral fertilization in papaya cultivation raises sustainability concerns and drives demand for validated organic alternatives. This study tested whether integrating humic substances (HS) and sugarcane filter cake (FC) would stimulate photosynthetic physiology, upregulate carbon metabolism gene expression, and increase fruit yield in ‘Golden’ papaya while outperforming conventional NPK fertilization. A 12-month field experiment was conducted in a randomized complete block design with a factorial arrangement of four HS doses (0, 90, 180, and 270 mL plant⁻¹) combined with two FC doses (0 and 60 kg plant⁻¹) plus an NPK control, measuring photosynthetic pigments, gas exchange, relative expression of rbcL, ACC oxidase, invertase, relative growth rate, and fruit yield. Combined HS and FC increased chlorophyll a by up to 205%, chlorophyll b by 277%, and carotenoids by 208% relative to unamended controls. Gene expression was strongly induced: rbcL reached 202-fold, invertase 156-fold, and ACC oxidase 84.8-fold above control values. Photosynthetic rate followed a quadratic dose-response peaking near 90 mL plant⁻¹ HS. Fruit yield nearly doubled under the optimal combined treatment (115 t ha⁻¹) compared with unamended controls (62 t ha⁻¹) and NPK fertilization (66 t ha⁻¹). These results confirm that HS and FC act synergistically as dual-purpose amendments, improving soil fertility while biostimulating papaya physiology through coordinated upregulation of photosynthetic capacity and carbon partitioning toward reproductive sinks. Full article

To address yield uncertainties from contrasting hydrothermal events in black soil regions, this study developed a universal estimation framework integrating multi-dimensional features. The universal yield estimation framework leveraged data from contrasting flood (2024) and drought (2025) scenarios in Youyi Farm in the Northeast Black Soil Region. And we fused multi-dimensional environmental features, including remote sensing, soil, and micro-topography factors, to identify “Regime Shifts” in yield-driving mechanisms across contrasting years. We evaluated four ML algorithms (RF, XGBoost, MLP, and TabNet) using Recursive Feature Elimination with Cross-Validation (RFECV) for variable optimization. Results showed the following: (1) The Universal RF model achieved superior robustness (R² = 0.80), overcoming inter-annual fluctuations. (2) Mechanistic analysis identified a “Regime Shift” in yield drivers, transitioning from micro-topography-governed “drainage limitation” during flooding to soil-texture-dominant (SAND) “linear limitation” during drought. (3) Dynamic growth-stage differential features successfully corrected asymmetric spectral responses, resolving slope inversion and overestimation driven by “non-productive greenness” during flooding. (4) Spatio-temporal yield mapping revealed a transition from topography-constrained linear distributions (2024) to soil-moisture-driven “patchy mosaic” structures (2025). Moran’s I increased from 0.21 to 0.45, reflecting intensified yield clustering and intensified spatial clustering under drought. This study provides a robust tool for food security monitoring and site-specific management in climate-vulnerable intensive agricultural zones. Full article

Soil water retention is a key factor in ecological processes regulating ecosystem stability and resilience under environmental stress. In this regard, marine-derived additives may provide sustainable strategies to enhance soil water dynamics. Here, novel biopolymers derived from thermophilic bacteria, including six exopolysaccharides (EPS1–EPS6) and four biosurfactants (BS1-BS4), and biomasses from seaweed (BM1–BM4) and marine cyanobacteria (BC1–BC2), were investigated for their wetting properties and soil water retention. Wetting properties, including reduction in contact angle (RCA) and atmospheric-air moisture uptake (AMU), were monitored for 36 h at constant temperature (30 °C). The effect on soil water retention was evaluated in terms of water loss of soil samples treated with two different concentrations (0.5 and 1% w/w) of either biopolymers or biomasses in a microcosm consisting of 10 g of soil and 10 mL of water, kept at a stable temperature of 22 °C for 200 h (until complete evaporation occurred). BC2 derived from Leptolyngbya sp. 43.3 was the best wetting agent (RCA = 39.44%), while the EPS4 produced by Bacillus horneckiae SBP3 was the best humectant agent (AMU = 179.63%). Soils amended with bacterial biopolymers (EPS4, EPS5, EPS6, BS1 and BS3), as well as biomasses derived from cyanobacteria BC2 and seaweed BM1–BM4, produced better improvement in soil water retention, with marked effects at the concentration of 1% w/w. The lipopeptide BS1 was the most effective in water loss reduction over a specific time of 96–125 h at both concentrations. These findings highlight the potential of these materials as nature-based solutions to improve soil-mediated ecosystem resilience to drought under climate change. Full article

Soil quality is central to agricultural sustainability and food security, yet coastal agroecosystems are increasingly threatened by degradation from intensive practices and seawater intrusion. This study aimed to integrate soil quality index (SQI), statistical modeling, machine learning (ML), and decision analysis to assess and manage soil health in the Skhirat coastal plain of Morocco. A total of 30 topsoil samples were collected and analyzed for chemical and nutrient properties. Spatial interpolation revealed strong coast–inland gradients where EC ranged from 0.47 to 6.3 dS/m with the highest salinity in the south-western fringe, while CEC (8.4–39.7 cmol/kg) and OM (0.54–2.81%) peaked inland. Principal component analysis (PCA) explained 65.9% of total variance, with salinity drivers loading negatively against fertility indicators. Redundancy analysis (RDA) biplots highlighted antagonism between salinity and fertility axes. The PCA-minimum data set (MDS)-SQI integrated key indicators and ranged from 0.084 to 0.897 (mean 0.614), classifying 33% of sites as low quality. The ML model linear regression achieved the best performance (R² = 0.907). Multi-criteria decision analysis (MCDA) using TOPSIS and PROMETHEE II prioritized coastal sites with indices up to 0.882, and robust underweight sensitivity (Spearman ρ = 0.992). This integrated framework demonstrates that soil chemical monitoring, AI prediction, and MCDA can jointly deliver robust, site-specific management strategies for vulnerable coastal agroecosystems. Full article

The analysis of specific habitat conditions involves a systematic assessment of environmental variables such as temperature, hydrology, and vegetation, to clarify species’ ecological requirements and develop conservation strategies. Common approaches include statistical modelling, various Habitat Suitability Index (HSI) models, and GIS-based spatial analyses, which quantify factors like topography, land cover and anthropogenic pressures. Today, machine learning (ML) methods are widely applied across engineering disciplines, including water resources management. In this study, ML methods, particularly model trees, are employed to model and predict key abiotic factors relevant to fish communities. The research focuses on the bioindicator species Barbus balcanicus (brook barbel), which inhabits the middle part of the Sutla River (transboundary river basin between Croatia and Slovenia) and serves as an indicator of ecological conditions in this system. Using ML, models for water depth, water velocity, and water temperature were developed and applied together with SWAT (Soil and Water Assessment Tool) data to determine the HSI for future scenarios to support habitat assessment and water management planning. Full article

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Pectinases are a group of enzymes widely applied in agri-food processes. This study aimed to isolate and characterize pectinase-producing yeasts and yeast-like fungi from soil and humus samples collected in a Moroccan argan forest, a region characterized by arid to semi-arid climatic conditions, with emphasis on screening and evaluating their pectinolytic activity. Among nine isolated strains, four exhibited detectable pectinolytic activity on pectin agar medium. Two promising isolates were molecularly identified by ITS region sequencing as Coniochaeta pulveracea PX765016 and Coniochaeta ligniaria PX765017. Notably, C. pulveracea PX765016 showed the highest pectinolytic potential, with a pectinolytic degradation index of 4.2 on pectin agar. This strain also exhibited maximal pectinase production after 96 h of submerged fermentation in YEPD medium under optimized conditions of pH 4, 30–35 °C, and 0.5% (w/v) pectin. The crude enzyme obtained under these conditions exhibited a specific activity of 559.90 ± 11.62 U/mg. The enzyme was subsequently subjected to sequential purification comprising ammonium sulfate precipitation, dialysis, and gel filtration chromatography on a Sephadex G-100 column, yielding a 2.99-fold purification with a final recovery of 14%. The purified enzyme exhibited optimal activity at pH 6.0 and 40–55 °C, with a reaction time of 20 min. Kinetic analysis of pectin hydrolysis revealed a Michaelis–Menten constant (Km) of 7.33 mg pectin per mL and a maximum reaction velocity (Vmax) of 1666.7 U/mg. To the best of our knowledge, this is the first report of pectinase production by a member of the genus Coniochaeta, and the first characterization of pectinase activity from C. pulveracea. Full article

Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon-rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable large-scale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). The review identifies hydrothermal liquefaction (HTL) as a strategically advantageous route for wet and heterogeneous biomass valorization, due to simultaneous yields of liquid biocrude, and solid hydrochar. The review emphasizes the application of biocrude upgradation processes like hydrodeoxygenation under biphasic solvent systems using sulfided NiMo and CoMo catalysts. Further, the review also establishes hydrochar as a tunable functional material rather than a mere byproduct for applications in fields of energy production, soil amendment, and heterogeneous catalysis. The review article examines technology readiness levels of different biomass valorization techniques, and suggests that while combustion, anaerobic digestion, torrefaction, and transesterification are commercially mature, HTL and carbon capture utilization and storage (CCUS)-integrated fuel synthesis pathways remain at intermediate readiness. Additionally, the review carries out an in-depth study on artificial intelligence and machine learning (AI and ML) applications in biomass valorization, where it observes that Tree-based ensemble models, particularly Random Forest and XGBoost, show strong performance for several HTL prediction tasks, while Gaussian Process Regression and neural network–Bayesian optimization approaches provide additional advantages for uncertainty estimation and process-level optimization. Finally, the future research opportunities in biomass valorization and AI/ML application in HTL-process optimization have been identified for improving the bio-based fuel production techniques. Full article

Ground improvement for foundations supported on soft soils is traditionally problematic because of low bearing capacity and a large magnitude of settlement. One sustainable method for mitigating these problems is the use of stone columns (SCs), particularly geosynthetic-encased stone columns (GESCs), to improve load transfer, confinement, and consolidation. This review critically synthesizes recent advances in the analysis and design of SC systems using experimental investigations, numerical simulations, and machine learning (ML)-based methodologies. The article indicates that GESCs, when integrated with modern data-driven techniques, especially hybrid metaheuristic ML models, represent a reliable and sustainable solution for soft soil stabilization. Traditional analytical and empirical methods remain useful; however, they are often inadequate for very soft soils (Undrained shear strength (c_u) < 15 kPa), where excessive bulging and large deformations dominate system behavior. Consequently, intelligent hybrid modeling approaches are emerging as the next generation of optimized, data-driven design tools in geotechnical engineering. Different failure mechanisms of SCs, including bulging, punching shear, and general shear failure, are critically discussed along with the governing design parameters. Previous studies consistently indicate that spacing ratios within the range of s/D = 2–3 can improve the bearing capacity ratio (BCR) by approximately 50–100%. Numerical and experimental studies further demonstrate that SC systems can transfer nearly 60–80% of the applied load through stress concentration and soil arching mechanisms. Furthermore, the application of geosynthetic encasement enhances the performance of SCs in very soft soils by increasing confinement, reducing lateral deformation, and enhancing bearing capacity by nearly 3–6 times compared with ordinary SCs. The review also evaluates the growing role of artificial intelligence techniques in forecasting settlement and bearing capacity behavior. ML techniques such as artificial neural networks (ANN), support vector regression (SVR), random forest (RF), XGBoost, and hybrid metaheuristic–ML models have shown high predictive capability, often achieving prediction errors below 5%. Despite these advancements, many existing ML studies still suffer from limited datasets, a lack of generalization, and insufficient incorporation of physical mechanisms. Full article

This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r² = 0.04–0.24, ubRMSE = 0.09–0.13 m³/m³), while SMAP performed better (r² = 0.18–0.62, ubRMSE = 0.05–0.07 m³/m³) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r² = 0.73; PM: R² = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r² = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI. Full article