Search Results (39,195)

An unmanned aerial vehicle (UAV) multi-spectral system provides a monitoring platform to rapidly obtain crop spectral information that can reflect crop nitrogen status for the generation of dynamic variable-rate nitrogen (VRN). To improve the accuracy of VRN prescription maps, a method of generating VRN prescription maps on the basis of the vegetation index was proposed, and the effects of UAV flight time and altitude on VRN prescription maps were analyzed. The experimental site was located in Dacaozhuang, Hebei Province, China, and the experimental crop was winter wheat (Lunxuan 145). The flight altitudes of the UAV system were set to 50, 70 and 90 m. The flight times were set to 8:00 a.m., 11:00 a.m., 2:00 p.m. and 5:00 p.m. local time. The flight area was 1.18 ha with a 60° rotation angle under a three-span center pivot irrigation system with an overhang. UAV flight missions were executed during the jointing, heading, and grain filling phases of winter wheat. There were 90 management zones with pie shapes in total, which were composed of a 10° angle in the rotation direction and 4 sprinklers along the lateral direction. The vegetation indices (VIs) which are closely related to crop nutrient status were selected and used to generate distribution maps, which were superimposed with the management zones to generate VRN prescription maps. The results demonstrated that the red-edge soil adjusted vegetation index (RESAVI) was relatively more sensitive to the nitrogen status of winter wheat than the other VIs were. The RESAVI distributions were stable during periods with a solar elevation angle greater than 50° (11:00 a.m.–2:00 p.m. local time), and the VRN prescription maps were similar, with the overlap percentage of the same fertilization grade being greater than 80% and the relative error of the fertilization amount being less than 5%. Compared with that at 2:00 p.m., the overlap percentage of the same fertilization grade was 56.6% in both seasons at 8:00 a.m., whereas flights at 5:00 p.m. exhibited overlaps of 70.9% and 44.6% in the 2023 and 2024 seasons, respectively. Conversely, the flight altitude had little influence on the fertilizer amount and VRN prescription maps. The difference in the amount of fertilizer used was less than 3% at different flight altitudes. The required time is half of that for a 50 m flight when the flight altitude is 70 m and one third of that when the flight altitude is 90 m. Our study recommended operating the UAV multi-spectral system at solar elevation angles greater than 50° when generating VRN prescription maps of winter wheat, and the flight height can be adjusted according to the field area and the endurance time of the UAV. Full article

4-Chlorophenoxyacetic acid (4-CPA) is an auxin-type plant growth regulator widely used in fruit and vegetable production. However, its influence on the nutritional and sensory qualities of horticultural crops remains insufficiently characterized. This study investigated the influence of 4-CPA application and oscillator-mediated pollination on the metabolic composition of fully ripe fruits of Solanum lycopersicum var. cerasiforme cv. ‘Zheyingfen No. 1’. Two concentrations of 4-CPA (16 mg/L and 8 mg/L) were applied during flowering, and their effects on amino acids, soluble sugars, organic acids, and volatile compounds (VOCs) were comparatively analyzed. The results indicated that treatment with 8 mg/L 4-CPA treatment significantly increased the total amino acid content in ripe fruits compared with the control and the 16 mg/L treatment. Among the 17 amino acids identified, the contents of umami-related amino acids, including glutamic acid (Glu) and aspartic acid (Asp), were markedly enhanced. In particular, Glu content in the C8 treatment was the highest and accounted for more than 50% of the total amino acid content. The accumulation of sugars was not significantly affected by 4-CPA treatment, while the C8 treatment resulted in the lowest level of total organic acids, which are crucial for flavor development at the ripening stage. A 29.35% increase in VOCs was observed” for conciseness in 4-CPA-treated fruits compared with the control. Analysis of relative odor activity values (rOAVs) showed that although 4-CPA treatment reduced the number of aroma-active compounds, it promoted the accumulation of β-ionone, thereby shifting the tomato fruit aroma profile toward floral, woody, sweet, and fruity notes. In summary, 4-CPA treatment regulated the nutritional and flavor quality of ripe cherry tomato fruits by increasing the content of Glu and other amino acids, enhancing the diversity of VOCs, and promoting the formation of key aroma-active substances such as β-ionone. Full article

Wheat (Triticum aestivum L.) is a key crop in Spain, especially in Castilla and León Region. However, there are few studies evaluating predictive models based on spectral indices and multivariate analysis to estimate yield in direct seeding (DS) and conventional seeding (CS) systems. This study addresses this need by implementing a split-plot experimental design in the city of Palencia, Spain, analyzing crop physiological data and nine spectral indices derived from multispectral aerial images captured by drones. The analysis included multivariate techniques such as Principal Component Analysis (PCA) and Random Forest (RF), supplemented with statistical tests, ROC curves, and prediction analysis. The results showed that the RF model successfully classified treatments with 93.75% accuracy and a Kappa index of 0.875, highlighting performance, nitrogen, and protein as key variables. Among the vegetation indices, the Soil-Adjusted Vegetation Index (SAVI) and the Advanced Vegetation Index (AVI) were the most relevant in the flowering stage, with ROC curve values of 0.7778 and 0.8025, respectively. Spearman’s correlations confirmed a significant relationship between these indices and key physiological variables, allowing to distinguish between DS and CS systems. The RF-based prediction model for performance showed R² values above 91% in the indices with the highest correlation. However, predictive capacity was higher in DS, suggesting that conditions inherent in non-mechanized handling significantly influence model performance. This highlights the importance of using non-destructive procedures to estimate production, enabling the development of adaptive and sustainable strategies that contribute to efficient agricultural production, since it is possible to anticipate crop yields before harvest, optimizing resources such as fertilizers and water. Full article

Farmland abandonment is increasingly prevalent, especially in the Central Himalaya. Precise mapping of abandoned areas is crucial for understanding their status and socioecological impacts. However, distinguishing abandoned farmland from transitional classes like fallow land and barren land is challenging without high-resolution satellite imagery and field verification. In this context, this work analyzes farmland abandonment in three ecological villages of the Nepal Himalaya using high-resolution satellite imagery and a people-to-pixel approach. First, the study villages were divided into grids based on their areas, and satellite imagery was printed for ground truthing. Second, ground truthing was conducted to identify active and abandoned farmland areas using the Field Area Measure App and satellite imagery. We measured the extent of abandoned farmland and assessed its current conditions. Third, the measured abandoned farmland shapefiles were exported for precise on-screen mapping using the Geographic Information System, alongside detailed land-cover mapping. Next, the accuracy assessment was performed using Google Earth satellite imagery, and the overall mapping accuracy was found to be 95.8%. Mapping results show that the highest areas of abandoned farmland were found in the Mountain region with 19.2% of total farmland, followed by the Hill region (12.7%) and the Tarai region (2.6%). Out of the total abandoned farmland, 49.2% is currently covered with bushes and shrubs, 42.9% with weeds and grasses, and the remaining 7.9% with woodlands. The findings emphasize the importance of integrating satellite technology with people engagement to address complex land-use challenges and offer critical insights for sustainable land management in the Nepal Himalaya and similar regions worldwide. Full article

Food loss and waste in school food services generate economic cost, environmental impacts, and social effects. Waste occurs in the final stages of the supply chain. It is particularly critical in educational institutions, leading to low nutrient intake during early stages of development and negatively impacting food security. Aiming to design a waste reduction strategy for the meal service of a preschool serving children aged 0–5 years, a descriptive observational study was conducted over a 6-month period. This study combined the measurement of the primary outcome (proportion of the served portion not consumed by food group) with the assessment of menu acceptability, the children’s food preferences, and the exploration of perceptions of both at-home caregivers and preschool professionals. Overall, the most frequent reasons for rejection were texture, preparation methods, and unfamiliarity with the food. The highest levels of waste were found in fruits and vegetables, with 17% left uneaten; protein-rich foods had a 15% waste rate, and cereals and tubers showed a 10% waste rate. Based on these findings, a family–school strategy is proposed that would increase household exposure to a wider variety of foods and establish periodic menu reviews to identify critical foods and ensure proper use in school food services. These results demonstrate that by enhancing food acceptance, we can decrease food waste, and in early stages, strengthen food security and nutritional use. Full article

The curation of large-scale, diverse datasets for robust weed detection is extremely time-consuming and resource-intensive in practice. Generative artificial intelligence (AI) opens up opportunities for image generation to supplement real-world image acquisition and annotation efforts. However, it is not a trial task to generate high-quality, multi-class weed images that capture the nuances and variations in visual representations for enhanced weed detection. This study presents a novel investigation of advanced stable diffusion (SD) integrated with a module with image prompt capability, IP-Adapter, for weed image generation. Using the IP-Adapter-based model, two image feature encoders, CLIP (contrastive language image pre-training) and BioCLIP (a vision foundation model for biological images), were utilized to generate weed instances, which were then inserted into existing weed images. Image generation and weed detection experiments are conducted on a 10-class weed dataset captured in vegetable fields. The perceptual quality of generated images is assessed in terms of Fréchet Inception Distance (FID) and Inception Score (IS). YOLOv11 (You Only Look Once version 11) models were trained for weed detection, achieving an improved mAP@50:95 of 1.26% on average when combining inserted weed instances with real ones in training, compared to using original images alone. Both the weed dataset and software programs in this study will be made publicly available. This study offers valuable perspectives into the use of IP-adapter-based SD for generating weed images and weed detection. Full article

The development of maize varieties with enhanced tolerance to drought stress has become a high-priority goal for maize breeding programs worldwide. In order to assess the variability of root and shoot traits in response to drought at an early vegetative stage, a set of 32 maize single-cross hybrids was grown under polyethylene glycol 8000-induced drought stress and well-watered control treatments. Drought stress significantly reduced hybrid seedling root and shoot lengths (RL and SL) as well as root and shoot fresh weights (RFW and SFW), while an increase in seedling root and shoot dry matter (RDM and SDM) and root fresh weight-to-shoot fresh weight ratio (RFW/SFW) was observed. The high heritability estimates for the four directly and easily measured traits, namely, RL, SL, RFW, and SFW (0.83, 0.83, 0.74, and 0.74, respectively), and medium-to-very-strong positive correlations among these traits under drought conditions indicate their applicability for the assessment of maize drought tolerance at the seedling stage and may represent a practical contribution to maize breeding programs for improved drought tolerance. Among the studied hybrids, hybrids 30, 3, and 23 were characterized by the largest RL under drought conditions and small relative change in RL between control and drought treatments. Hybrid 30 also showed one of the smallest relative reductions in SL, RFW, and SFW between the two treatments, while hybrids 3 and 23 were among those which exhibited the highest relative RL/SL and RFW/SFW increase between the two treatments, which supports their potential as parental lines in drought-tolerant breeding. Full article

Tropospheric ozone (O₃) pollution is a major concern in urban environments because of its toxicity for both people and vegetation. This paper review provides an overview of atmospheric mechanisms, as well as the potential and best management practices of urban greening for reducing O₃ pollution in cities. Urban greening has often been proposed as a cost-effective solution to reduce O₃ pollution, but its effectiveness depends on careful species selection and integration with broader air quality management strategies. Ozone is a secondary pollutant and the volatile organic compounds emitted by vegetation (BVOCs) can play a prominent role in O₃ formation. A list of recommended and to-avoid species is given here to drive future planting at city scale. Planting low BVOC-emitting species and combining greening with reductions in anthropogenic emissions are key to maximizing benefits and minimizing unintended increases in O₃. Public and non-public institutions should carefully select plant species in consultation with expert scientists from the early stages, e.g., by considering local conditions and pollutant dynamics to design effective greening interventions. Collaborative planning among urban ecologists, atmospheric scientists, and municipalities is thus crucial to ensure that greening interventions contribute to overall air quality improvements rather than inadvertently enhancing O₃ formation. Such improvements will also translate into plant protection from O₃ stress. Therefore, future directions of research and policy integration to achieve healthier, O₃-resilient urban ecosystems are also provided. Full article

Accurate land cover mapping is essential for environmental monitoring and agricultural management. Sentinel-2 imagery, with high spatial resolution and open access, provides valuable opportunities for operational classification. Convolutional neural networks (CNNs) have demonstrated state-of-the-art results, yet their adoption is limited by high computational demands and limited methodological transparency. This study proposes a lightweight CNN for soil–vegetation classification, in Dolj County, Romania. The architecture integrates three convolutional blocks, global average pooling, and dropout, with fewer than 150,000 trainable parameters. A fully documented workflow was implemented, covering preprocessing, patch extraction, training, and evaluation, addressing reproducibility challenges common in deep leaning studies. Experiments on Sentinel-2 imagery achieved 91.2% overall accuracy and a Cohen’s kappa of 0.82. These results are competitive with larger CNNs while reducing computational requirements by over 90%. Comparative analyses showed improvements over an NDVI baseline and a favorable efficiency–accuracy balance relative to heavier CNNs reported in the literature. A complementary ablation analysis confirmed that the adopted three-block architecture provides the optimal trade-off between accuracy and efficiency, empirically validating the robustness of the proposed design. These findings highlight the potential of lightweight, transparent deep learning for scalable and reproducible land cover monitoring, with prospects for extension to multi-class mapping, multi-temporal analysis, and fusion with complementary data such as SAR. This work provides a methodological basis for operational applications in resource-constrained environments. Full article

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity. Full article

This study investigates the influence of urban greenery on microclimate conditions in Novi Sad, a city characterized by a temperate oceanic climate, by integrating high-resolution remote sensing data with in situ measurements from 12 urban climate stations. Sentinel-2 imagery was used to capture vegetation patterns, including tree lines and small green patches, while air temperature data were collected across two climatically contrasting years. Vegetation extent and structural characteristics were quantified using NDVI thresholds (0.6–0.8), capturing variability in vegetation activity and canopy density. Results indicate that high-activity vegetation, particularly dense tree canopies, exerts the strongest cooling effects, significantly influencing air temperatures up to 750 m from measurement sites, whereas total green area alone showed no significant effect. Cooling effects were most pronounced during summer and autumn, with temperature reductions of up to 2 °C in areas dominated by mature trees. Diurnal–nocturnal analyses revealed consistent spatial cooling patterns, while seasonal variability highlighted the role of evergreen and deciduous composition. Findings underscore that urban heat mitigation is driven more by vegetation structure and composition than by green area size, emphasizing the importance of preserving high-canopy trees in urban planning. This multidimensional approach provides actionable insights for optimizing urban greenery to enhance microclimate resilience. Full article

The use of biostimulants and corroborants is increasing worldwide. Laboratory and field assays show their effectiveness in improving the vegetative performance of plants and their tolerance to abiotic stresses. This study aims to evaluate the in vitro activity of a biostimulant, based on pine bark extract, against some fungal phytopathogens. This research was carried out at the Laboratory of Plant Pathology (SAAF Department, University of Palermo, Italy), employing the poison food technique. Artificial agar media (Potato Dextrose Agar, PDA), simple or added with different concentrations of the biostimulant, were used to evaluate the differences in diametral growth of the fungi Aspergillus niger, Aspergillus tubingensis, Botrytis cinerea, Coriolopsis gallica, Fomitiporia mediterranea, Fusarium oxysporum, Pleurostoma richardsiae and Pleurotus ostreatus. The biostimulant was shown to contain the growth of most of the tested fungi, with the greatest effectiveness on A. tubingensis, C. gallica, F. mediterranea and P. richardsiae at the highest concentration, moderate effects on A. niger, F. oxysporum and P. ostreatus and no effect on B. cinerea. The observed fungistatic effects suggest that this biostimulant could contribute to integrated disease management while supporting more sustainable crop protection practices. In vivo tests aimed at evaluating the efficacy of these products on the evolution of different diseases in the field are ongoing, and preliminary results are promising but they are part of future work. Full article

Urban park areas mitigate urbanization’s negative impacts by integrating environmental, social and cultural benefits. Development strategies should enable participation and consider all user groups’ needs, following sustainability principles. However, ensuring multifunctionality often generates conflicting decisions. While the universal necessity for safety is widely acknowledged, its implementation frequently results in the diminution of a crucial sense of privacy. For example, the universally recognized need for safety may compromise the willingness sense of privacy or intimacy. This can discourage those for whom this need is important and prevent urban parks from fully utilizing their social potential. This study examines how spatial configurations of plant forms within urban parks shape personal experiences. We used an intra-group design to evaluate photographs of park spaces, manipulated using Photoshop AI algorithms to examine safety, privacy, preference, and willingness to spend time. Variables included space size and shape. The study used Computer-Assisted Web Interviewing (CAWI) with 300 participants. Regression and mediation analyses showed willingness to visit derives from space attractiveness, influenced by perceived safety and privacy. Analyses revealed the following: open areas were safest but the least private, corridor spaces were the least safe but the most private; curtain screens enhanced perception better than corridor screens; small spaces with corridor screens were least attractive; space size mattered less for open spaces than screened spaces; and spatial configuration was critical in assessing small spaces. The findings of this research enhance our comprehension of the perception of park spaces. They hold potential practical implications for sustainable design, facilitating the development of plant forms that are more socially effective, particularly those with substantial environmental value, such as dense vegetation that serves as visual screens. Neglecting these preferences may result in inappropriate design decisions that fail to accommodate users’ needs and behaviors, thereby not fully capitalizing on the potential of urban green spaces. Full article

Decontamination of wastewater, industrial effluents, stormwater, and graywater can be carried out through the use of natural or constructed wetlands. In either case, the natural functions of soil, vegetation, and organisms are widely applied for the treatment of contaminated water. In particular, in the physical design of a constructed wetland, several operational factors must be adjusted with the aim of reducing pollution levels. Although various fully customized design methodologies have been developed and reported in the literature, they often fail to meet the required decontamination objectives. In this context, the application of the NSGA-II evolutionary algorithm is adequate to optimize the physical design of a horizontal subsurface flow wetland for graywater treatment, focusing specifically on the removal of biodegradable organic matter (BOD${}_5$). Four competing objectives are considered: minimizing physical volume and total design cost, while maximizing contaminant removal efficiency and graywater flow rate. Five constraint functions are also incorporated: removal efficiency greater than 95%, physical volume below 1000 m${}^3$, flow rate above 10 m${}^3$/d, a limit on total construction cost of MXN 1,000,000, and maintaining a length-to-width ratio greater than or equal to 2 but less than or equal to 4. The proposed methodology generates a wide set of non-dominated solutions, visualized through Pareto surfaces, which highlight the trade-offs among different objectives. This approach offers the possibility of selecting optimal designs under specific conditions, which underscores the limitations of conventional single-solution models. The results show that the methodology consistently achieved removal efficiencies above 95%, with construction costs within budget and physical volumes below the established limit, offering a more versatile and cost-effective alternative. This work demonstrates that the integration of NSGA-II into wetland design is an effective and adaptable strategy, capable of providing sustainable alternatives for graywater treatment and constituting a valuable decision-making tool. Full article

This study proposed a methodology for obtaining a valid database of food retail establishments and characterized the community food environment, understood as the distribution and type of food outlets, in a Brazilian medium-sized municipality after the collapse of a mining tailings dam. An ecological study was conducted with establishments selling food for home consumption (butcher shops, fish markets; fruit and vegetable specialty markets; large- and small-chain supermarkets; bakeries and local markets) and immediate consumption (bars, snack bars, and restaurants). For home-consumption establishments, data were requested from governments and completed with website/app searches, virtual audits (Google Street View), and on-site audits. For immediate-consumption establishments, only on-site audit was used due to the low quality of the secondary databases. Agreement between databases was assessed with the Kappa statistic. Density (d) was calculated by the area (in km²) of the sampling stratum. Public databases presented low validity (23.0%; Kappa −0.388; p = 1.000), even after virtual auditing (31.4%; Kappa 0.37; p < 0.001). 96 establishments for home consumption and 261 for immediate consumption were identified, with predominance of local markets (35.4%), bars (35.2%), and snack bars (29.1%). The region with the highest density of establishments was the “Other Areas” stratum (d = 4.7 for home-consumption establishments and d = 13.2 for immediate-consumption establishments). Audit proved most effective, especially for small establishments. The lack of governmental databases and the identified food environment should inform municipal policies to promote food and nutrition security and reduce inequalities after the disaster. Full article