Search Results (102)

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application. Full article

Climate change has intensified the frequency and severity of forest disturbances globally, including windthrow, which poses substantial risks for both forest productivity and ecosystem stability. Rapid and precise assessment of wind-induced tree damage is essential for effective management, yet many injuries remain visually undetectable in the early stages. This study employed drone-based multispectral imaging and a simulated wind stress experiment (static pulling) on Norway spruce (Picea abies (L.) Karst.) to investigate the detectability of physiological and structural changes over four years. Multispectral data were collected at multiple time points (2023–2024), and a suite of vegetation indices (the Normalised Difference Vegetation Index (NDVI), the Structure Insensitive Pigment Index (SIPI), the Difference Vegetation Index (DVI), and Red Edge-based indices) were calculated and analysed using mixed-effects models. Our results demonstrate that trees subjected to mechanical bending (“Bent”) exhibit substantial reductions in the near-infrared (NIR)-based indices, while healthy trees maintain higher and more stable index values. Structure- and pigment-sensitive indices (e.g., the Modified Chlorophyll Absorption Ratio Index (MCARI 2), the Transformed Chlorophyll Absorption in Reflectance Index/Optimised Soil-Adjusted Vegetation Index (TCARI/OSAVI), and RDVI) showed the highest diagnostic value for differentiating between damaged and healthy trees. We found the clear identification of group- and season-specific patterns, revealing that the most pronounced physiological decline in Bent trees emerged only several seasons after the disturbance. Full article

Co-infection of Orientia tsutsugamushi and influenza A virus complicates diagnosis and treatment in endemic regions because of overlapping clinical features and potential synergistic inflammation. We describe a 68-year-old woman from Hainan, China, who presented with five days of high fever (39.2 °C), nonproductive cough, eschar formation, lymphadenopathy, cytopenias, elevated liver enzymes, and raised inflammatory markers. On the day of admission, influenza A was confirmed by rapid antigen test and Orientia tsutsugamushi IgM/IgG was detected via colloidal-gold immunochromatography, prompting concurrent oseltamivir and doxycycline therapy. Quantitative PCR on day 2 measured an Orientia tsutsugamushi load of 2.85 × 10⁴ copies/mL (Cq 28.86), and targeted next-generation sequencing on day 3 revealed a high H1N1pdm09 viral burden (>1 × 10⁶ copies/mL) with low-level human herpesvirus 1 co-detection. Nested PCR and Sanger sequencing assigned Orientia tsutsugamushi to the Karp_A lineage and influenza A to clade 6B.1A.5a.2a. The patient defervesced by hospital day 2, laboratory indices normalized by day 3, and radiographic abnormalities resolved by day 6. This first documented Orientia tsutsugamushi–influenza A co-infection in China highlights the value of integrating rapid serology, qPCR quantification, nested PCR genotyping, and tNGS for early, precise dual-pathogen identification. Systematic multi-pathogen screening during overlapping transmission seasons is recommended to guide timely combination therapy and enhance epidemiological surveillance. Full article

Early-season sugarcane identification plays a pivotal role in precision agriculture, enabling timely yield forecasting and informed policy-making. Compared to post-season crop identification, early-season identification faces unique challenges, including incomplete temporal observations and spectral ambiguity among crop types in early seasons. Previous studies have not systematically investigated the capability of optical and synthetic aperture radar (SAR) data for early-season sugarcane identification, which may result in suboptimal accuracy and delayed identification timelines. Both the timing for reliable identification (≥90% accuracy) and the earliest achievable timepoint matching post-season level remain undetermined, and which features are effective in the early-season identification is still unknown. To address these questions, this study integrated Sentinel-1 and Sentinel-2 data, extracted 10 spectral indices and 8 SAR features, and employed a random forest classifier for early-season sugarcane identification by means of progressive temporal analysis. It was found that LSWI (Land Surface Water Index) performed best among 18 individual features. Through the feature set accumulation, the seven-dimensional feature set (LSWI, IRECI (Inverted Red-Edge Chlorophyll Index), EVI (Enhanced Vegetation Index), PSSRa (Pigment Specific Simple Ratio a), NDVI (Normalized Difference Vegetation Index), VH backscatter coefficient, and REIP (Red-Edge Inflection Point Index)) achieved the earliest attainment of 90% accuracy by 30 June (early-elongation stage), with peak accuracy (92.80% F1-score) comparable to post-season accuracy reached by 19 August (mid-elongation stage). The early-season sugarcane maps demonstrated high agreement with post-season maps. The 30 June map achieved 88.01% field-level and 90.22% area-level consistency, while the 19 August map reached 91.58% and 93.11%, respectively. The results demonstrate that sugarcane can be reliably identified with accuracy comparable to post-season mapping as early as six months prior to harvest through the integration of optical and SAR data. This study develops a robust approach for early-season sugarcane identification, which could fundamentally enhance precision agriculture operations through timely crop status assessment. Full article

Human metapneumovirus (hMPV), a member of the Pneumoviridae subfamily, has emerged as a significant etiological agent of acute respiratory tract infections across diverse age groups, particularly affecting infants, the elderly, and immunocompromised individuals. Since its initial identification in 2001, hMPV has been recognized globally for its seasonal circulation pattern, predominantly in late winter and spring. hMPV is a leading etiological agent, accounting for approximately 5% to 10% of hospitalizations among pediatric patients with acute respiratory tract infections. hMPV infection can result in severe bronchiolitis and pneumonia, particularly in young children, with clinical manifestations often indistinguishable from those caused by human RSV. Primary hMPV infection typically occurs during early childhood; however, re-infections are frequent and may occur throughout an individual’s lifetime. hMPV is an enveloped, negative-sense RNA virus transmitted through respiratory droplets and aerosols, with a 3–5-day incubation period. The host immune response is marked by elevated pro-inflammatory cytokines, which contribute to disease severity. Advances in molecular diagnostics, particularly reverse transcription–quantitative polymerase chain reaction (RT-qPCR) and metagenomic next-generation sequencing (mNGS), have improved detection accuracy and efficiency. Despite these advancements, treatment remains largely supportive, as no specific antiviral therapy has yet been approved. Promising developments in vaccine research, including mRNA-based candidates, are currently undergoing clinical evaluation. This review synthesizes current knowledge on hMPV, highlighting its virological, epidemiological, and clinical characteristics, along with diagnostic advancements and emerging therapeutic strategies, while underscoring the critical role of continued research and sustained preventive measures—including vaccines, monoclonal antibodies, and non-pharmaceutical interventions—in mitigating the global burden of hMPV-related disease. Full article

Blueberry is a high-value fruit crop in the United States, with Georgia and Florida serving as important early-season production regions. In these areas, several thrips species (Thysanoptera: Thripidae), including Frankliniella tritici (Fitch), Frankliniella bispinosa (Morgan), and Scirtothrips dorsalis (Hood), have emerged as economically significant pests. While F. tritici and F. bispinosa primarily damage floral tissues, S. dorsalis targets young foliage. Their rapid reproduction, high mobility, and broad host range contribute to rapid population buildup and complicate the management programs. Species identification is often difficult due to overlapping morphological features and requires the use of molecular diagnostic tools for accurate identification. Although action thresholds, such as 2–6 F. tritici per flower cluster, are used to guide management decisions, robust economic thresholds based on yield loss remain undeveloped. Integrated pest management (IPM) practices include regular monitoring, cultural control (e.g., pruning, reflective mulch), biological control using Orius insidiosus (Say) and predatory mites, and chemical control. Reduced-risk insecticides like spinetoram and spinosad offer effective suppression while minimizing harm to pollinators and beneficial insects. However, the brief flowering period limits the establishment of biological control agents. Developing species-specific economic thresholds and phenology-based IPM strategies is critical for effective and sustainable thrips management in blueberry cropping systems. Full article

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

Early-season crop mapping plays a critical role in yield estimation, agricultural management, and policy-making. However, most existing methods assign a uniform earliest identification time across provincial or broader extents, overlooking spatial heterogeneity in crop phenology and environmental conditions. This often results in delayed detection or reduced mapping accuracy. To address this issue, we proposed a zonal-based early-season mapping framework for winter wheat by integrating phenological and environmental factors. Aggregation zones across Shandong Province were delineated using Principal Component Analysis (PCA) based on factors such as start of season, end of season, temperature, slope, and others. On this basis, early-season winter wheat identification was conducted for each zone individually. Training samples were generated using the Time-Weighted Dynamic Time Warping (TWDTW) method. Time-series datasets derived from Sentinel-1/2 imagery (2021–2022) were processed on the Google Earth Engine (GEE) platform, followed by feature selection and classification using the Random Forest (RF) algorithm. Results indicated that Shandong Province was divided into four zones (A–D), with Zone D (southwestern Shandong) achieving the earliest mapping by early December with an overall accuracy (OA) of 97.0%. Other zones reached optimal timing between late December and late January, all with OA above 95%. The zonal strategy improved OA by 3.6% compared to the non-zonal approach, demonstrated a high correlation with official municipal-level statistics (R² = 0.97), and surpassed the ChinaWheat10 and ChinaWheatMap10 datasets in terms of crop differentiation and boundary delineation. Historical validation using 2017–2018 data from Liaocheng City, a prefecture-level city in Shandong Province, achieved an OA of 0.98 and an F₁ score of 0.96, further confirming the temporal robustness of the proposed approach. This zonal strategy significantly enhances the accuracy and timeliness of early-season winter wheat mapping at a large scale. Full article

Sorghum (Sorghum bicolor, (L.) Moench.), is one of the most important cereals in semi-arid and subtropical regions of Africa. However, in these regions, sorghum cultivation is often faced with several constraints. In Mali, terminal or post-flowering drought, caused by the early cessation of rains towards the end of the rainy season, is one of the most common constraints. Sorghum is generally adapted to harsh conditions. However, drought combined to heat reduce its yield and production in tropical and subtropical regions. To identify parents of sorghum hybrids tolerant to post-flowering drought for commercial hybrids development and deployment, a total of 200 genotypes, including male and female parents of the hybrids, were evaluated in 2022 by lysimeters under two water regimes, well-irrigated and water-stressed, at ICRISAT in Niger. Agronomic traits such as phenological stages, physiological traits including transpiration efficiency, and morphological traits such as green leaf number were recorded. Genotype × environment (G × E) interaction was significant for harvest index (HI), green leaf number (GLN), and transpiration efficiency (TE), indicating different responses of genotypes under varying water conditions. Transpiration efficiency (TE) was significantly and positively correlated with total biomass (BT), harvest index (HI), and grain weight (GW) under both stress conditions. Genotypes ICSV216094, ICSB293, ICSV1049, ICSV1460016, and ICSV216074 performed better under optimal and stress conditions. The Principal Component Analysis (PCA) results led to the identification of three groups of genotypes. The Groups 1 and 3 are characterized by their yield stability and better performance under stress and optimal conditions. These two groups could be used by breeding programs to develop high yield and drought tolerant hybrids. Full article

The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the Agricultural Production Systems sIMulator (APSIM) to simulate the historical yield of double-season rice in Jiangxi Province from 2000 to 2018. The methodological advancements included the following: the localized parameter optimization of APSIM using the Nelder–Mead simplex algorithm and NSGA-II multi-objective genetic algorithm to adapt to regional rice varieties, enhancing model robustness; coarse-resolution yield simulations (10 km grids) driven by meteorological, soil, and management data; and high-resolution refinement (1 km grids) via HASM, which fused APSIM outputs with station-observed yields as optimization constraints, resolving the trade-off between accuracy and spatial granularity. The results showed that the following: (1) Compared to the APSIM model, the HASM-APSIM model demonstrated higher accuracy and reliability in simulating historical yields of double-season rice. For early rice, the R-value increased by 14.67% (0.75→0.86), RMSE decreased by 34.02% (838.50→553.21 kg/hm²), MAE decreased by 31.43% (670.92→460.03 kg/hm²), and MAPE dropped from 11.03% to 7.65%. For late rice, the R-value improved by 27.42% (0.62→0.79), RMSE decreased by 36.75% (959.0→606.58 kg/hm²), MAE reduced by 26.37% (718.05→528.72 kg/hm²), and MAPE declined from 11.05% to 8.08%. (2) Significant spatiotemporal variations in double-season rice yields were observed in Jiangxi Province. Temporally, the simulated yields of early and late rice aligned with statistical yields in terms of numerical distribution and interannual trends, but simulated yields exhibited greater fluctuations. Spatially, high-yield zones for early rice were concentrated in the eastern and central regions, while late rice high-yield areas were predominantly distributed around Poyang Lake. The 1 km resolution outputs enabled the precise identification of yield heterogeneity, supporting targeted agricultural interventions. (3) The growth rate of double-season rice yield is slowing down. To safeguard food security, the study area needs to boost the development of high-yield and high-quality crop varieties and adopt region-specific strategies. The model proposed in this study offers a novel approach for simulating crop yield at the regional scale. The findings provide a scientific basis for agricultural production planning and decision-making in Jiangxi Province and help promote the sustainable development of the double-season rice industry. Full article

Though a variety of methods are used to conduct West Nile virus (WNV) surveillance, accurate prediction and prevention of outbreaks remain a global challenge. Previous studies have established that the concentration of antibodies to mosquito saliva is directly related to the intensity of exposure to mosquito bites and can be a good proxy to determine risk of infection in human populations. To assess exposure characteristics and transmission dynamics among avian communities, we tested the levels of IgY antibodies against whole salivary glands of Aedes albopictus and Culex quinquefasciatus, as well as WNV antigen, in 300 Northern cardinals sampled from April 2019 to October 2019 in St. Tammany Parish, Louisiana. Though there were no significant differences in antibody responses among sex or age groups, exposure to Ae. albopictus bites was more positively associated with exposure to WNV compared with Cx. quinquefasciatus exposure (ρ = 0.2525, p < 0.001; ρ = 0.1752, p = 0.02437). This association was more pronounced among female birds (ρ = 0.3004, p = 0.0075), while no significant relationship existed between exposure to either mosquito vector and WNV among male birds in the study. In general, two seasonal trends in exposure were found, noting that exposure to Ae. albopictus becomes less intense throughout the season (ρ = −0.1529, p = 0.04984), while recaptured birds in the study were found to have increased exposure to Cx. quinquefasciatus by the end of the season (ρ = 0.277, p = 0.0468). Additionally, we report the identification of several immunogenic salivary proteins, including D7 family proteins, from both mosquito vectors among the birds. Our results suggest that Ae. albopictus may have a role in early-season transmission of WNV, particularly among brooding females and hatchling cardinals. However, bloodmeal analysis was not included in this work and further studies are needed to verify this assumption. Yet, broad circulation of WNV in nesting avian communities could enhance risk of infection among Cx. quinquefasciatus mosquitoes in the late season, with the potential to contribute to human disease incidence and epizootic spillover in the environment. Full article

Respiratory viruses are widespread in the community, affecting both the upper and lower respiratory tract. This review provides an updated synthesis of the epidemiology, pathophysiology, clinical impact, and management of severe respiratory viral infections in critically ill patients, with a focus on immunocompetent adults. The clinical presentation is typically nonspecific, making etiological diagnosis challenging. This limitation has been mitigated by the advent of molecular diagnostics—particularly multiplex PCR (mPCR)—which has not only improved pathogen identification at the bedside but also significantly reshaped our understanding of the epidemiology of respiratory viral infections. Routine mPCR testing has revealed that respiratory viruses are implicated in 30–40% of community-acquired pneumonia hospitalizations and are a frequent trigger of acute decompensations in patients with chronic comorbidities. While some viruses follow seasonal patterns, others circulate year-round. Influenza viruses and Pneumoviridae, including respiratory syncytial virus and human metapneumovirus, remain the principal viral pathogens associated with severe outcomes, particularly acute respiratory failure and mortality. Bacterial co-infections are also common and substantially increase both morbidity and mortality. Despite the growing contribution of respiratory viruses to the burden of critical illness, effective antiviral therapies remain limited. Neuraminidase inhibitors remain the cornerstone of treatment for severe influenza, whereas therapeutic options for other respiratory viruses are largely lacking. Optimizing early diagnosis, refining antiviral strategies, and systematically addressing bacterial co-infections are critical to improving outcomes in patients with severe viral pneumonia. Full article

It is important to comprehend the evolution of drought characteristics and the relationships between different kinds of droughts for effective drought mitigation and early warnings. The study area was the Pearl River Basin, where spatiotemporal changes in the multiscale water balance and soil moisture at various depths were analyzed. The meteorological data used in this study were derived from the China Meteorological Forcing Dataset, while the soil moisture data were obtained from the ECMWF ERA5-Land reanalysis dataset. The Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI) were applied to represent meteorological and agricultural droughts, respectively. By using the run theory for drought event identification, the characteristic values of drought events were analyzed. The correlation between the multiscale SPEI and SSI was examined to represent the propagation time from meteorological drought to agricultural drought. This study indicated that while the western part of the Pearl River Basin experienced a worsening atmospheric moisture deficit and the southern part had intensifying dry conditions for soil moisture, the rest of the basin remained relatively moist and stable. Soil conditions were moister in the deeper soil layers. The durations of agricultural droughts have generally been shorter than those of meteorological droughts over the past 40 years. Within the top three soil layers, the severity, duration, and frequency of drought events progressively increased, increased, and decreased, respectively, as soil depth increased. The propagation time scale from a meteorological drought to a four-layer agricultural drought was typically within 1–5 months. This study advanced existing research by systematically analyzing drought propagation times across soil depths and seasons in the Pearl River Basin. The methodology in this study is applicable to other basins to analyze drought complexities under climate change, contributing to global drought resilience strategies. Understanding the spatiotemporal characteristics of meteorological and agricultural droughts and the propagation time between them can help farmers and agricultural departments predict droughts and take appropriate drought-resistant measures to alleviate the damage of droughts on agricultural production. Full article

Respiratory syncytial virus (RSV) is a significant cause of respiratory infections in adults, particularly among older adults and individuals with chronic diseases. While traditionally linked to pediatric populations, RSV’s impact on adults, especially the elderly, is increasingly recognized but remains understudied in many regions. This retrospective study, conducted at the University Hospital Center Zagreb from October 2022 to April 2024, is the first to analyze RSV-positive adults in Croatia. Using RT-PCR testing, we evaluated clinical and epidemiological characteristics in both hospitalized and outpatient populations, focusing on those aged > 65 years. Among 2631 tested individuals, the RSV prevalence was 5.25%, with older adults experiencing the most severe outcomes, including pneumonia, COPD exacerbation, and intensive care admissions. Seasonal analysis confirmed a winter peak in RSV cases, while chronic conditions such as cardiovascular and respiratory diseases were strongly associated with higher complication rates. These findings demonstrate that older adults with comorbidities bear the greatest burden of RSV infection, highlighting the need for the early identification of high-risk patients. By providing detailed insights into RSV-related outcomes in this population, this study supports the development of targeted prevention and management strategies to reduce the burden of RSV in vulnerable groups. Full article

Climate change has increased the risk of snow drought, which is associated with a deficit in snowfall and snowpack. The objectives of this research are to improve drought identification in a warming climate by developing a new snow-based hydroclimatic aggregate drought index (SHADI) and to assess the impacts of snowpack and snowmelt in drought analyses. To derive the SHADI, an R-mode principal component analysis is performed on precipitation, snowpack, surface runoff, and soil water storage. Then, a joint probability distribution function of drought frequencies and drought classes, conditional expectation, and k-means clustering are used to categorize droughts. The SHADI was applied to the Red River of the North Basin (RRB), a typical cold climate region, to characterize droughts in a mostly dry period from 2003 to 2007. The SHADI was compared with the hydroclimatic aggregate drought index (HADI) and U.S. drought monitor (USDM) data. Cluster analysis was also utilized as a benchmark to compare the results of the HADI and SHADI. The SHADI showed better alignment with cluster analysis results than the HADI, closely matching the identified dry/wet conditions in the RRB. The major differences between the SHADI and HADI were observed in cold seasons and in transition periods (dry to wet or wet to dry). The derived variable threshold levels for different categories of drought based on the SHADI were close to, but different from, those of the HADI. The SHADI can be used for short-term lead prediction of droughts in cold climate regions and, in particular, can provide an early warning for drought in the warming climate. Full article