Search Results (74)

Winter wheat is a major staple crop worldwide, and real-time monitoring of soil moisture content (SMC) is critical for yield security. Targeting the monitoring needs under arid conditions in southern Xinjiang, this study proposes a UAV multispectral-based SMC estimation method that constructs novel three-dimensional (3-D) texture indices. Field experiments were conducted over two consecutive growing seasons in Kunyu City, southern Xinjiang, China, with four irrigation and four fertilization levels. High-resolution multispectral imagery was acquired at the jointing stage using a UAV-mounted camera. From the imagery, conventional texture features were extracted, and six two-dimensional (2-D) and four 3-D texture indices were constructed. A correlation matrix approach was used to screen feature combinations significantly associated with SMC. Random forest (RF), partial least squares regression (PLSR), and back-propagation neural networks (BPNN) were then used to develop SMC models for three soil depths (0–20, 20–40, and 40–60 cm). Results showed that estimation accuracy for the shallow layer (0–20 cm) was markedly higher than for the middle and deep layers. Under single-source input, using 3-D texture indices (Combination 3) with RF achieved the best shallow-layer performance (validation R² = 0.827, RMSE = 0.534, MRE = 2.686%). With multi-source fusion inputs (Combination 7: texture features + 2-D texture indices + 3-D texture indices) combined with RF, shallow-layer SMC estimation further improved (R² = 0.890, RMSE = 0.395, MRE = 1.91%). Relative to models using only conventional texture features, fusion increased R² by approximately 11.4%, 11.7%, and 18.1% for the shallow, middle, and deep layers, respectively. The findings indicate that 3-D texture indices (e.g., DTTI), which integrate multi-band texture information, more comprehensively capture canopy spatial structure and are more sensitive to shallow-layer moisture dynamics. Multi-source fusion provides complementary information and substantially enhances model accuracy. The proposed approach offers a new pathway for accurate SMC monitoring in arid croplands and is of practical significance for remote sensing-based moisture estimation and precision irrigation. Full article

We develop a year-round, field-scale framework to retrieve soil moisture and map irrigation in an arid irrigation district where crop phenology and canopy dynamics undermine static, single-season approaches. However, the currently popular TVDI application is limited during non-growing seasons. To address this gap, we introduce a season-stratified TVDI scheme—based on the LST–EVI feature space with phenology-specific dry/wet edges—coupled with a non-growing-season inversion that fuses Sentinel-1 SAR and Landsat features and compares multiple regressors (PLSR, RF, XGBoost, and CNN). The study leverages 2023–2024 multi-sensor image time series for the Yichang sub-district of the Hetao Irrigation District (China), together with in situ topsoil moisture, meteorological records, a local cropping calendar, and district statistics for validation. Methodologically, EVI is preferred over NDVI to mitigate saturation under dense canopies; season-specific edge fitting stabilizes TVDI, while cross-validated regressors yield robust soil-moisture retrievals outside the growing period, with the CNN achieving the highest accuracy (test R² ≈ 0.56–0.61), outperforming PLSR/RF/XGBoost by approximately 12–38%. The integrated mapping reveals complementary seasonal irrigation patterns: spring irrigates about 40–45% of farmland (e.g., 43.39% on 20 May 2024), summer peaks around 70% (e.g., 71.42% on 16 August 2024), and autumn stabilizes near 20–25% (e.g., 24.55% on 23 November 2024), with marked spatial contrasts between intensively irrigated southwest blocks and drier northeastern zones. We conclude that season-stratified edges and multi-source inversions together enable reproducible, year-round irrigation detection at field scale. These results provide operational evidence to refine irrigation scheduling and water allocation, and support drought-risk management and precision water governance in arid irrigation districts. Full article

Background: Human nasal myiasis is a rare zoonotic infection caused by Oestrus ovis with a non-specific clinical presentation that can mimic more common conditions such as allergic rhinitis. Objective: To report a series of nasal myiasis cases in immunocompetent individuals, emphasizing the clinical presentation and complementary investigations (endoscopic findings, parasitological identification, skin prick tests, and imaging studies) that facilitate differential diagnosis from allergic rhinitis and enable early treatment. Methods: We conducted a retrospective study including cases of nasal myasis diagnosed and managed at the ENT department of the Military Hospital of Tunis over an 18-year period (2007–2025). Demographic, clinical, diagnostic, and therapeutic data were analyzed. Results: The mean age was 43 years, with a female predominance. Most patients presented with acute rhinological symptoms initially suggestive of allergic rhinitis. Nasal endoscopy revealed larvae in 79% of cases with parasitological confirmation of Oestrus ovis. Facial CT scans performed in five cases (36%) were unremarkable. Management consisted of multiple daily nasal saline irrigations and albendazole, in association with oral corticosteroids and antihistamines, resulting in symptom resolution within an average of 4 days. Conclusions: Nasal myiasis should be considered in atypical or treatment-resistant rhinitis. Nasal endoscopy is essential for diagnosis. Full article

Sustainable nitrogen (N) management in arable crops requires the real-time assessment of crop growth and N uptake, particularly in water-limited environments. In the present study, we conducted two large-scale field experiments with rainfed and irrigated wheat in South-East Turkey to evaluate the effectiveness of drone- and satellite-based spectral indices, in combination with neural network models, for estimating biomass and nitrogen uptake. Four N fertilizer rates in the irrigated fields (N₀: 0, N₆: 60, N₁₂: 120, and N₁₆: 160 kg N ha⁻¹) and five N rates in the rainfed fields (N₀: 0, N₂: 20, N₄: 40, N₅: 50, and N₆: 60 kg N ha⁻¹) were tested. Highest fresh biomass was 57.7 ± 1.1 and 15.9 ± 1.0 t/ha⁻¹ for irrigated and rainfed treatments, respectively, with 2.5-fold higher grain yield in irrigated (8.2 ± 1.2 t/ha⁻¹) compared to rainfed (2.9 ± 0.9 t/ha⁻¹) wheat. Drone-based spectral indices, especially those based on the red-edge region (CL_{Red_edge}), correlated strongly with biomass (R² > 0.9 in irrigated wheat) but failed to explain crop N concentration throughout the vegetation period. This limitation was attributed to the nitrogen dilution effect, where increasing biomass during crop growth leads to a decline in the concentration of nitrogen, complicating its accurate estimation via remote sensing. To address this, we employed a two-layer feed-forward neural network model and used SPAD and plant height values as supplementary input parameters to enhance estimations based on vegetation indices. This approach substantially enhanced the predictions of N uptake (R² up to 0.95), while even simplified model version using only NDVI and plant height parameters achieved significant performance (R² = 0.84). Overall, our results showed that spectral indices are reliable predictors of biomass but insufficient for estimating nitrogen concentration or uptake. Integrating indices with complementary crop traits in nonlinear models provides acceptable estimates of N uptake, supporting more precise fertilizer management and sustainable wheat production under water-limited conditions. Full article

Land use/cover (LUCC) change in arid oasis–desert ecotones has significant implications for spatial governance in ecologically fragile regions. To better capture the temporal and spatial complexity of land transitions, this study developed a vector tracing method by integrating time-series remote sensing data with vector-based transfer pathways. Analysis of northern Oman from 1995 to 2020 revealed the following: (1) Arable land and impervious surfaces expanded from 0.51% to 1.09% and from 0.31% to 0.98%, respectively, while sand declined from 99.03% to 97.01%. Spatially, arable land was concentrated in piedmont irrigation zones, impervious surfaces near coastal cities, and shrubland and grassland along the Al-Hajar Mountains, forming a complementary land use mosaic. (2) Human activities were the dominant driver, with typical one-way chains accounting for 69.76% of total change. Sand was mainly transformed into arable land (7_C1, 7_D1, 7_E1; where the first part denotes the original type, the letter denotes the year of change, and the last digit denotes the new type), impervious surfaces (7_C6, 7_D6, 7_E6), and shrubland (7_E4). (3) Water scarcity and an arid climate remained primary constraints, manifested in typical reciprocating chains in the oasis–desert interface (7_D1_E7, 7_A1_B7, 7_C1_D7) and in the arid vegetation zone along the Al-Hajar Mountain foothills (7_D3_E7, 7_C3_D7), together accounting for 24.50% of total change. (4) The region exhibited coordinated transitions among oasis, urban, and ecological land, avoiding the common conflict of cropland loss to urbanization. During the study period, transitions among arable land, impervious surfaces, forest, shrubland, and wetland were rare (Type 16: 3.31%, Type 82: 2.89%, Type 12: 0.04%, Type 18: 0.01%). The case of northern Oman provides a valuable reference for collaborative spatial governance in ecologically fragile arid zones. Future research should integrate socio-economic drivers, climate change projections, and higher-temporal-resolution data to enhance the applicability of the chain-spectrum method in other arid regions. Full article

Nasal irrigation (NI) is an effective, safe, low-cost strategy for treating and preventing upper respiratory tract diseases. High-volume, low-pressure saline irrigations are the most efficient method for removing infectious agents, allergens, and inflammatory mediators. This article reviews clinical evidence supporting NI use in various conditions: nasal congestion in infants, recurrent respiratory infections, acute and chronic rhinosinusitis, allergic and gestational rhinitis, empty nose syndrome, and post-endoscopic sinus surgery care. NI improves symptoms, reduces recurrence, enhances the efficacy of topical drugs, and decreases the need for antibiotics and decongestants. During the COVID-19 pandemic, NI has also been explored as a complementary measure to reduce viral load. Due to the safe profile and mechanical cleansing action on inflammatory mucus, nasal irrigations represent a valuable adjunctive treatment across a wide range of sinonasal conditions. Full article

The western Shanxi Loess region, as a typical semi-arid ecologically fragile zone, faces severe soil and water resource constraints. The apple–peanut intercropping system can significantly improve water productivity and economic benefits through complementary resource utilization, representing an effective approach for sustainable agricultural development in the region. This study took the apple–peanut intercropping system as the research object (apple variety: ‘Yanfu 8’; peanut variety: ‘Huayu 38’), setting three peanut planting densities (D1: 27,500 plants/ha; D2: 18,333 plants/ha; D3: 10,833 plants/ha) and two water regulation measures—W1 (irrigation upper limit at 85% of field capacity, FC) and W2 (65% FC), with non-irrigated controls (CK) at different planting densities for comparison. This study systematically analyzed the synergistic regulation effects of intercropping density and water management on system water use and comprehensive benefits. Results showed that medium planting density combined with medium irrigation (W2D2 treatment) could maximize intercropping advantages, effectively improving the intercropping system’s soil water content (SWC), yield (GY), and water use efficiency (WUE). This research provides a theoretical basis for precision irrigation in fruit–crop intercropping systems in semi-arid regions. However, based on the significant water-saving and yield-increasing effects observed in the current experimental year, follow-up studies should verify its stability through multi-year fixed-position observation data. Full article

The agronomic effectiveness of biofertilizers is influenced by strain origin, genetic identity, crop genotype, soil type, and environmental conditions. For best results, both the plant and rhizobia strain must be adapted to the common harsh soil conditions in the tropics. While plant varieties have changed over the years, complementary research on new strains effectiveness under varying soil fertility conditions has lagged in southern Africa. Seven field experiments were established in the main soybean-producing areas of Zimbabwe in the north, central, and north–east regions to evaluate agronomic benefits of new rhizobia strains against the current exotic commercial strain (MAR1491). One site was irrigated (site 3), and the other six sites were rainfed (sites 1, 2, 4, 5, 6, and 7). While trends in inoculation response varied from site to site due to site conditions, inoculation with the strains NAZ15, NAZ25, and NAK128 consistently yielded high grain yields, which were similar to the current commercial strain MAR1491 and to application of mineral fertilizer (51.75 and 100 kg N ha⁻¹). Grain yield levels were generally below 2 t ha⁻¹ for sites 2, 3, and 5 and above 2 t ha⁻¹ for sites 1, 4, and 6, while for the irrigated site 3, they ranged upwards of 3 t ha⁻¹. When irrigated, all strains except NAK9 performed similarly in terms of grain yields and aboveground N uptake. Further testing on the inclusion of the indigenous strains NAZ15, NAZ25, and NAK128 in multi-strain commercial inoculant production targeting application in regions and soils where they excel beyond the current exotic strain MAR1491 is recommended. Full article

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article

The increasing global demand for food, combined with rising climate extremes, is driving agricultural expansion—often without sufficient consideration for sustainability. Greenhouse agriculture presents a promising solution to address the dual challenges of food security and climate change mitigation. This study models potential scenarios for the expansion of greenhouse agriculture in Imbabura Province, Ecuador, while adhering to sustainability criteria. Two widely used methods were compared: the Analytical Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS) and the Maximum Entropy (MaxEnt) model. The GIS-AHP method relies on expert-defined weights, whereas the MaxEnt model utilizes the probabilistic distribution of presence-only data, enabling a complementary evaluation of both subjective and data-driven approaches. Both models incorporated various factors, including topographic, climatic, hydrological, ecological, infrastructural, agricultural, and soil-related variables. The results classified the territory into five levels of suitability for greenhouse expansion. The GIS-AHP model identified 20,761.64 hectares as highly suitable, while the MaxEnt model identified only 5618.15 hectares. This discrepancy highlights the differing influences of various factors: In the GIS-AHP, land cover/use, irrigation availability, and proximity to existing greenhouses were the most influential. In contrast, in the MaxEnt model, proximity to greenhouses was the dominant factor. These findings not only provide a spatially explicit foundation for sustainable territorial planning but also contribute methodologically by integrating both data-driven and expert-driven approaches. This supports evidence-based policy-making in fragile Andean ecosystems. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article

Untreated domestic wastewater discharged into rivers and streams severely deteriorates water quality and aquatic ecosystems, especially in regions lacking adequate treatment infrastructure. This study aimed to evaluate the effectiveness of phytoremediation of domestic wastewater by the Sector Popular natural wetland (Mexico), located adjacent to fluvial and crossing structures. The evaluation was conducted by comparing contamination levels in the influent and effluent water, based on Mexican Official Standards (NOM-001-SEMARNAT-1996, NOM-003-SEMARNAT-1997, and NOM-001-SEMARNAT-2021), as well as several water quality indicators for irrigation. The wetland reduced concentrations of five-day biochemical oxygen demand by 98%, chemical oxygen demand by 95%, total suspended solids by 96%, total nitrogen by 92%, total phosphorus by 67%, and fecal coliforms by 96%. However, the treated water did not meet reuse standards for public services due to elevated salinity and residual presence of fecal microorganisms. These findings confirm that natural wetlands can significantly improve the quality of domestic wastewater and help mitigate environmental degradation in rivers. This approach represents a feasible and complementary strategy for wastewater treatment in regions with similar hydrological and infrastructure conditions. Full article

Water-quality monitoring in agricultural irrigation systems is challenging due to the dynamic and heterogeneous nature of mixed water sources, which complicates traditional and remote sensing-based assessment methods. Traditional water quality monitoring relies on water sampling and laboratory analysis, which can be time-consuming, labor-intensive, and spatially limited. In situ hyperspectral reflectance sensing (HRS) presents a promising alternative, offering high-resolution, non-invasive monitoring capabilities. However, applying HRS in mixed-water environments—where served-water effluent, precipitation, and natural river water converge—presents significant challenges due to variability in water composition and environmental conditions. While HRS has been widely explored in controlled or homogeneous water bodies, its application in highly dynamic agricultural mixed-water systems remains understudied. This study addresses this gap by evaluating the relationships between in situ hyperspectral data (450–900 nm) and key water-quality parameters—pH, turbidity, nitrates, and chlorophyll-a—across three campaigns in a Colombian tropical agricultural irrigation system. A Pearson’s correlation analysis revealed the strongest spectral associations for nitrates, with positive correlations at 500 nm (r ≈ 0.76) and 700 nm (r ≈ 0.85) and negative correlations in the near-infrared (850 nm, r ≈ −0.88). Conversely, the pH exhibited weak and diffuse correlations, with a maximum of r ≈ 0.51. Despite their optical activity, turbidity and chlorophyll-a showed unexpectedly weak correlations, likely due to the optical complexity of the mixed water matrix. Random Forest regression identified key spectral regions for each parameter, yet model performance was limited, with R² values ranging from 0.51 (pH) to −1.30 (chlorophyll-a), and RMSE values between 0.41 and 1.51, reflecting the challenges of predictive modeling in spatially and temporally heterogeneous wastewater systems. Despite these challenges, this study establishes a baseline for future hyperspectral applications in complex agricultural water monitoring and highlights critical spectral regions for further investigation. To improve the feasibility of HRS in mixed-water assessments, future research should focus on enhancing data-preprocessing techniques, integrating complementary sensing modalities, and refining predictive models to better account for environmental variability. Full article

Objectives: This study aimed to evaluate the efficacy of cleaning in minimally shaped mesial and oval distal canals of 3D models of mandibular molars, focusing on positive pressure irrigation, wireless and conventional passive ultrasonic irrigation (PUI), and diode laser (DL) at 980 nm. Methods: Forty-four 3D-printed resin models, based on eleven natural mandibular molars (each with mesial and distal canals), were divided into four groups (n = 11 per group) to evaluate different irrigation methods. A total of 22 mesial canals (size 20/.04) and 11 oval distal canals (size 25/.04) were analyzed per group. Each root canal was uniformly filled with an artificial hydrogel to simulate a biofilm mixture. Following this preparation, the specified irrigation techniques were applied to the respective groups. Quantitative evaluations of pre- and post-irrigation images were performed to assess the efficiency of tissue removal along the entire length of the canal and in the apical, middle, and coronal thirds. Results: The findings revealed no significant differences in the initial amount of tissue between the samples, indicating uniform filling. In the apical region of mesial canals, conventional PUI showed the highest cleaning efficiency (14.1% residual tissue), significantly outperforming the other methods (p < 0.05). Cordless PUI and DL also surpassed positive pressure irrigation, leaving 30.4% and 29.3% residual tissue, respectively, compared to 42.2% with positive pressure. In the middle third, all methods tested performed better than needle irrigation (p < 0.05), but there were no significant differences in the coronal third or over the full canal length. Distal oval canals showed no significant differences in cleaning effectiveness among methods. Conclusions: Although no single method was superior regarding the full canal length, supplementary techniques such as PUI and DL offer potential benefits over conventional irrigation methods, particularly in the apical third of the canal. Complementary approaches such as conventional PUI and diode laser at 980 nm showed superior cleaning efficiency, particularly in the apical third. These results suggest their integration could improve the effectiveness of cleaning in minimally instrumented mesial canals. Full article