Search Results (56)

Effective detection of center-pivot irrigation systems is crucial in understanding agricultural activity and managing groundwater resources for sustainable uses, especially in semi-arid regions such as North Dakota, where irrigation primarily depends on groundwater resources. In this study, we have adopted YOLOv11 to detect the center-pivot irrigation systems using multiple remote sensing datasets, including Landsat 8, Sentinel-2, and NAIP (National Agriculture Imagery Program). We developed an ArcGIS custom tool to facilitate data preparation and large-scale model execution for YOLOv11, which was not included in the ArcGIS Pro deep learning package. YOLOv11 was compared against other popular deep learning model architectures such as U-Net, Faster R-CNN, and Mask R-CNN. YOLOv11, using Landsat 8 panchromatic data, achieved the highest detection accuracy (precision: 0.98; recall: 0.91; and F1-score: 0.94) among all tested datasets and models. Spatial autocorrelation and hotspot analysis revealed systematic prediction errors, suggesting a need to adjust training data regionally. Our research demonstrates the potential of deep learning in combination with GIS-based workflows for large-scale irrigation system analysis, adopting precision agricultural technologies for sustainable water resource management. Full article

This paper presents an innovative solution to address agricultural irrigation needs through a hybrid renewable energy system (HRES) that was specifically designed for a farm located in the Skikda region of Algeria. This system is tailored to irrigate 830 fruit trees spread across 3 hectares with a total perimeter of 770 m. The proposed approach integrates two main renewable energy sources (while eliminating the use of traditional batteries for electrical energy storage): solar and wind. Instead, a large water reservoir is employed as an energy storage medium in the form of potential energy. Utilizing gravity, this reservoir directly powers the irrigation system for the fruit trees, thereby reducing the costs and environmental impacts associated with conventional batteries. This innovative design not only enhances sustainability, but also improves the system’s energy efficiency. To ensure precise and customized sizing of the system for the irrigation area, a detailed mathematical modeling of the key system components (solar panels, wind turbines, and reservoir) was conducted. This modeling identifies the critical design variables required to meet technical specifications and irrigation needs. A multi-objective optimization approach was then developed to determine the optimal configuration of the HRES, and this was achieved by considering both technical and economic constraints. The optimization algorithm used was tailored to the formulated problem, ensuring reliable and applicable results. The robustness of the optimization approach was shown by the precise match between energy production (24 kWh at 16,119.40 $) and the minimum demand. This alignment prevents over- or under-designing the system, which increases costs and reduces energy use. The findings highlight the relevance and effectiveness of the proposed methodology, demonstrating its practical utility and significant potential for generalization and adaptation to different agricultural zones with varying conditions. This work paves the way for sustainable and innovative solutions for agricultural irrigation, particularly in remote areas or regions lacking traditional energy infrastructure. Full article

Western Kazakhstan is susceptible to desertification, with surface water resource scarcity constraining agricultural development. Groundwater has substantial potential as a reliable and secure alternative to other water resources, particularly for irrigation, which is required to ensure food security. Eight aquifer segments with an exploitable potential of 0.24 km³/year have been identified for the integrated assessment of groundwater’s suitability for irrigation. The assessment criteria included hydro-chemical groundwater characteristics and irrigated land soil-reclamation conditions. The primary objectives of this study were to assess the groundwater quality for irrigation and to develop a practical operation scheme for rational groundwater use in water-saving irrigation technologies and optimize agricultural crop cultivation. Approximately 90% of the groundwater in these aquifer segments was found to be suitable for irrigation, with a total amount of 6520 thousand m³/day and a salinity of up to 1 g/L, and an additional 12,971 thousand m³/day had a water salinity of up to 3 g/L. Only approximately 10% had TDS values above 3 g/L and up to 6.5 g/L, categorized as conditionally suitable for restricted customized agricultural crop irrigation. Irrigated land development by complex soil desalination agro-reclamation operations enabled the use of brackish water for irrigation. The integrated analysis allowed the development of drip irrigation and sprinkling system irrigation schemes that gradually replaced wasteful surface irrigation. The irrigated land prospective area recommended for groundwater irrigation development is 653 km², with the further restructuring of cultivated areas, reducing the number of annual grasses and grain crops and increasing the number of vegetables, potatoes, and perennial grasses. Full article

In the last decade, the use of surgical guides in dentistry has expanded to include endodontic surgery, yet most studies have focused on accuracy rather than potential heat generation. This in vitro study evaluated heat generation during bone drilling with custom-made bone trephines, both with and without static surgical guides, and compared the results to those of 2 mm pilot drills. Drilling was performed on porcine rib bone specimens under controlled conditions, with heat generation measured using an infrared thermometer. None of the groups exceeded the critical temperature of 47 °C; although, the guided trephine group recorded the highest peak temperature (7.9 °C above baseline). Significant differences in heat increments were observed among the groups. Post hoc analyses revealed that the guided pilot drill produced significantly lower heat increments compared to the trephine groups, particularly during the penetration of the second cortical layer and at peak temperatures (p < 0.05). The use of a surgical guide did not limit the cooling and lubricating effects of irrigation in the trephine groups. Regression analyses confirmed a strong relationship between drilling time and temperature increase, with guided trephines showing a steeper temperature rise compared to pilot drills. These findings emphasize the importance of proper irrigation, sharp instruments, reduced drilling speeds, and careful technique to minimize heat generation during guided bone drilling procedures. Full article

The precise identification of wheat seedlings in unmanned aerial vehicle (UAV) imagery is fundamental for implementing precision agricultural practices such as targeted pesticide application and irrigation management. This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from variable illumination conditions and (2) morphological characteristics of wheat seedlings characterized by slender leaf structures and flexible posture variations. To address these challenges, we propose DHS-YOLO, a novel deep learning framework optimized for robust wheat seedling detection under diverse illumination intensities. Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. Second, the Histogram Transformer (HT) module integrates a dynamic-range spatial attention mechanism to mitigate illumination-induced image degradation. Third, we implement the ShapeIoU loss function that prioritizes geometric consistency between predicted and ground truth bounding boxes, particularly optimizing for slender plant structures. The experimental validation was conducted using a custom UAV-captured dataset containing wheat seedling images under varying illumination conditions. Compared to the existing models, the proposed model achieved the best performance with precision, recall, mAP50, and mAP50-95 values of 94.1%, 91.0%, 95.2%, and 81.9%, respectively. These results demonstrate our model’s effectiveness in overcoming illumination variations while maintaining high sensitivity to fine plant structures. This research contributes an optimized computer vision solution for precision agriculture applications, particularly enabling automated field management systems through reliable crop detection in challenging environmental conditions. Full article

Vineyard is one of the most complex and vulnerable agroecosystems, and ongoing climate change makes it necessary to identify effective management and adaptation practices. For this reason, a water balance model tailored for viticulture was developed to be implemented within a Decision Support System (DSS) aimed at supporting winemakers both in the vineyard’s planning and management phase. Starting from a simple monthly water balance, based on the Thornthwaite–Mather method, the model returns the water stress risk class through the connection to a soil and climate database; the user can however customize the response by inserting information related to a specific vineyard (e.g., planting, soil, and management layout). The model was tested using data from a three-year field experiment carried out in a vineyard under permanent grass cover (PG) or continuous tillage (CT), allowing for the evaluation of its performance in terms of water balance estimation. The model provided results consistent with the measured soil moisture values, and the annual risk of water stress corresponds to what was measured in the field, differing at most by only one class. The model can guide the user in finding the best solutions for designing new vineyards or managing the inter-row by simulating the adoption of different strategies (trellis system, planting density, type of cover crop or soil tillage) or suggesting alternative solutions (needs of irrigation supply, more suitable cultivars, or rootstocks). Full article

The perennial species Hypericum perforatum, commonly known as St. John’s Wort, is well regarded for its medicinal attributes, particularly its strong anti-inflammatory and antidepressant effects. Hypericum perforatum L., commonly known as balsam, is extensively employed in both traditional and contemporary medicine due to its biological properties, although the plant’s medicine distribution is limited to Europe and Asia. This study pioneers the investigation of Hypericum perforatum cultivation in a Mediterranean country, specifically Greece, focusing on the effects of irrigation and biostimulants of two distinct genotypes on quantitative (height, drug yield, essential oil yield) and qualitative (essential oil content and composition) characteristics. A field trial was conducted at the experimental farm of the Agrotechnology Department at the University of Thessaly, located in the Larissa region. This study investigated various testing varieties under different irrigation levels and biostimulant applications. The results underscore the importance of customized irrigation and biostimulant strategies in improving yield and quality during the second growing season, establishing a foundation for sustainable agricultural progress. Notably, irrigated treatments significantly increased plant height, dry biomass yield, and essential oil production per hectare. Specifically, the essential oil yields for irrigated treatments were nearly double those of rainfed treatments, with 219 kg/ha for rainfed and 407 kg/ha for irrigated. The genotype played a crucial role in influencing production potential, height, flowering, and essential oil composition, with one variety demonstrating biennial blooming and modified essential oil compounds. While irrigation positively impacted yield, it also reduced certain essential oil compounds while increasing β-pinene content. The effects of biostimulants varied based on their composition, with some enhancing and others diminishing essential oil content. Notably, the biostimulant containing algae with auxin and cytokinin (B2) proved to be the most effective in improving the therapeutic profile. This study offers valuable insights into the cultivation of H. perforatum in a Mediterranean climate, highlighting the necessity for ongoing research into native populations, irrigation levels, biostimulants, fertilization, and other factors that affect crop yield and quality characteristics. Full article

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning. Full article

In this paper, we focused on the advancement of Dam Monitoring Software that incorporates the Finite Element Method (FEM), as these large infrastructure constructions are crucial for ensuring a dependable water supply, irrigation, flood control, renewable electric energy generation, and safe operation, which is of utmost importance to any country. However, the material properties and geotechnical environments of dams can change (deteriorate) over time, while the standards and legal norms that govern them become more and more rigorous, so in order to accurately assess the state of a dam and detect any concerning behavior, the software must be updated as well. The custom-developed FEM solver, unlike many commercial alternatives, is adaptable and can be reconfigured to function within a Dam Monitoring System. In this paper, we present the procedure for interpolating numerical values at measurement points, when the position of the measurement point does not align with the node of the element, allowing for additional instrument locations to be added to the monitored system without the need for remeshing the numerical model. This procedure is used to compare the actual pore pressures and temperature values of the concrete dam structure with the prediction of the numerical model, and the agreement is much greater with the new interpolation algorithm in comparison to the nearest nodal values, with the average relative difference for pore pressure reduced from 8.89% to 8.10%, justifying this implementation. Full article

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations. Full article

Inner Mongolia, a major region in China for growing sugar beet, faces challenges caused by unscientific water and fertilizer management. This mismanagement restricts the improvement of sugar beet yield and quality and exacerbates water waste and environmental pollution. This study aims to evaluate the effects of reduced water and fertilizer on the growth and physiological metabolism of sugar beet taproot. Field experiments were conducted in Ulanqab, Inner Mongolia, in 2022 and 2023, using a split-plot design with three levels each of fertilization and irrigation. The study analyzed the effects of reduced water and fertilizer treatments on fresh taproot weight, respiration rate, energy metabolism, respiratory enzyme activity, and gene expression in sugar beet taproot. It was found that a 10% reduction in fertilizer significantly increased the beet taproot fresh weight. Further research revealed that during the rapid leaf growth phase and the taproot and sugar growth period, a 10% reduction in fertilizer upregulated HK and IDH gene expression and downregulated G6PDH gene expression in the beet taproot. This increased HK and IDH activities, decreased G6PDH activity, enhanced the activity of the EMP-TCA pathway, and inhibited the PPP. Taproot weight was positively correlated with the respiration rate, ATP content, EC, and ATPase, HK, and IDH activities, thereby increasing the taproot growth rate and taproot fresh weight, with an average increase of 4.0% over two years. These findings introduce a novel method for optimizing fertilizer use, particularly beneficial in water-scarce regions. Implementing this strategy could help farmers in western Inner Mongolia and similar areas improve crop yield and sustainability. This study offers new insights into resource-efficient agricultural practices, highlighting the importance of customized fertilization strategies tailored to local environmental conditions. Full article

Ecuador is one of the world’s leading banana exporters; however, low productivity resulting from inadequate agronomic management requires an analysis of banana production units. This study aimed to define the types of banana production units based on the different agronomic management practices adopted by producers in two Ecuadorian provinces. Data from the National Institute of Statistics and Censuses (INEC) for 2021 were used, with a sample of 319 production units. Principal component and cluster analyses were applied to identify the different types of production units, resulting in four types: high technology conventional (Cluster 1), balanced conventional (Cluster 2), intensive conventional (Cluster 3), and agroecological (Cluster 4). It is important to highlight that 58% of the production units are intensive conventional and use an average of 3.5 management practices, with 98% using fertilizers, 100% using fungicides and pesticides, and 45% using improved genotypes. In contrast, agroecological production is still incipient in Ecuador (4.7%). Regression analysis showed that waste is important in high-yield production units in the three clusters. In addition, Cluster 2 relied on regional factors, family labor, and irrigation efficiency, while in intensive conventional farms (Cluster 3), banana yield was related to fungicide application. Therefore, public policies should be customized according to cluster-specific characteristics to optimize agronomic management practices and facilitate their transfer among groups. Full article

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality, but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach. Full article

In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps. Full article

Considering the degradation of water resources and the increase in human population, desalination seems to be a promising method for meeting the global water demand, from potable water to plant irrigation needs. The contribution of desalination to the agricultural sector, through the supply of water for plants or animals, is critical because this sector represents 70% of the global water demand. Unfortunately, the desalination process is energy-intensive and subjected to several factors that result in undesirable fluctuations on quality/quantity of product water, and/or energy waste. Renewable energy sources can supply the necessary power, but they amplify these challenges because their availability varies over time. A simple and efficient way to tackle this issue is to control the pressure of the feed water before feeding it to the membrane. Typically, the pairing control systems are quite expensive or lack the necessary customization freedom that could improve their operation. Therefore, this study highlights the feasibility of enhancing a typical desalination control equipment via the incorporation of modern low-cost microcontrollers and flexible open-source software; the potential of these tools has not yet been fully explored. The microcontroller executes customized PID logic, driving an industrial inverter module. Our results indicate that the proposed system can keep pace with the desalination process setpoints, reducing the stress of the electromechanical components and periods of out-of-specification freshwater production. This low-level control function implementation minimizes the need for human intervention while providing a promising foundation for further extensions and customizations in this area. Full article