Search Results (808)

The maize tassel represents one of the most pivotal organs dictating maize yield and quality. Investigating its phenotypic information constitutes an exceedingly crucial task within the realm of breeding work, given that an optimal tassel structure is fundamental for attaining high maize yields. High-throughput phenotyping technologies furnish significant tools to augment the efficiency of analyzing maize tassel phenotypic information. Towards this end, we engineered a fully automated multi-angle digital imaging apparatus dedicated to maize tassels. This device was employed to capture images of tassels from 1227 inbred maize lines falling under three genotype classifications (NSS, TST, and SS). By leveraging the 3D reconstruction algorithm SFM (Structure from Motion), we promptly obtained point clouds of the maize tassels. Subsequently, we harnessed the TreeQSM algorithm, which is custom-designed for extracting tree topological structures, to extract 11 archetypal structural phenotypic parameters of the maize tassels. These encompassed main spike diameter, crown height, main spike length, stem length, stem diameter, the number of branches, total branch length, average crown diameter, maximum crown diameter, convex hull volume, and crown area. Finally, we compared the GFC (Gaussian Fuzzy Clustering algorithm) used in this study with commonly used algorithms, such as RF (Random Forest), SVM (Support Vector Machine), and BPNN (BP Neural Network), as well as k-Means, HCM (Hierarchical), and FCM (Fuzzy C-Means). We then conducted a correlation analysis between the extracted phenotypic parameters of the maize tassel structure and the genotypes of the maize materials. The research results showed that the Gaussian Fuzzy Clustering algorithm was the optimal choice for clustering maize genotypes. Specifically, its classification accuracies for the Non-Stiff Stalk (NSS) genotype and the Tropical and Subtropical (TST) genotype reached 67.7% and 78.5%, respectively. Moreover, among the materials with different maize genotypes, the number of branches, the total branch length, and the main spike length were the three indicators with the highest variability, while the crown volume, the average crown diameter, and the crown area were the three indicators with the lowest variability. This not only provided an important reference for the in-depth exploration of the variability of the phenotypic parameters of maize tassels but also opened up a new approach for screening breeding materials. Full article

Mapping constitutes a critical component of architectural heritage research, providing the groundwork for both conservation and utilization efforts. Three-dimensional (3D) digital documentation represents a prominent form of mapping in the contemporary era, and its value is widely recognized. However, cost and portability constraints often limit its widespread use in routine research and conservation initiatives. This study proposes a cost-effective and portable approach to 3D digital documentation, employing everyday-carry (EDC) equipment, the iPhone 15 Pro and DJI Mini 4 Pro, for data acquisition in architectural heritage. The workflow was subsequently optimized, and the datasets from the iPhone-LiDAR and microdrone were seamlessly integrated, resulting in an integrated 3D digital model of both the indoor and outdoor spaces of the architectural heritage site. The model demonstrated an overall relative error of 4.93%, achieving centimeter-level accuracy, precise spatial alignment between indoor and outdoor sections, clear and smooth texture mapping, high visibility, and suitability for digital display applications. This optimized workflow leverages the strengths of both EDC equipment types while addressing the limitations identified in prior studies. Full article

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings. So, the scene knowledge will include global context information, fitting fine-grained visual information and scene structure information. Then, a scene filter module is designed to learn the global context information and fitting fine-grained visual information, and a scene structure module is designed to learn the scene structure information. Finally, the scene semantic features are used as the carrier to integrate three categories of information into the relative scene features, which can assist in the recognition of the occluded fittings and the tiny-scale fittings after feature mining and feature integration. The experiments show that the proposed network can effectively improve the performance of the multi-fitting detection task compared with the Faster R-CNN and other state-of-the-art models. In particular, the detection performances of the occluded and tiny-scale fittings are significantly improved. Full article

Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks detail and a triangulated model can hide gaps. The challenge is to generate a dense cloud with field-deployable tools. Traditional dense reconstruction methods can take several dozen hours on low-capacity systems like laptops or embedded units. To speed up this process, we propose building the dense cloud incrementally within an SfM framework while incorporating data redundancy management to eliminate recalculations and filtering already-processed data. The method evaluates overlap area limits and computes depths by propagating the matching around SeaPoints—the keypoints we design for identifying reliable areas regardless of the quality of the processed underwater images. This produces local partial dense clouds, which are aggregated into a common frame via the SfM pipeline to produce the global dense cloud. Compared to the production of complete dense local clouds, this approach reduces the computation time by about 70% while maintaining a comparable final density. The underlying prospect of this work is to enable real-time completeness estimation directly on board, allowing for the dynamic re-planning of the acquisition trajectory. Full article

Depth completion is widely employed in Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM), which are of great significance to the development of autonomous driving. Recently, the methods based on the fusion of vision transformer (ViT) and convolution have brought the accuracy to a new level. However, there are still two shortcomings that need to be solved. On the one hand, for the poor performance of ViT in details, this paper proposes a semi-convolutional vision transformer to optimize local continuity and designs a geometric perception module to learn the positional correlation and geometric features of sparse points in three-dimensional space to perceive the geometric structures in depth maps for optimizing the recovery of edges and transparent areas. On the other hand, previous methods implement single-stage fusion to directly concatenate or add the outputs of ViT and convolution, resulting in incomplete fusion of the two, especially in complex outdoor scenes, which will generate lots of outliers and ripples. This paper proposes a novel double-stage fusion strategy, applying learnable confidence after self-attention to flexibly learn the weight of local features. Our network achieves state-of-the-art (SoTA) performance with the NYU-Depth-v2 Dataset and the KITTI Depth Completion Dataset. It is worth mentioning that the root mean square error (RMSE) of our model on the NYU-Depth-v2 Dataset is 87.9 mm, which is currently the best among all algorithms. At the end of the article, we also verified the generalization ability in real road scenes. Full article

Community-acquired urinary tract infections (UTIs) represent a significant public health issue, primarily due to the increasing antibiotic resistance among uropathogens. This study assesses the resistance status of uropathogenic community Enterobacterales to various antibiotics, particularly aminoglycosides, and determines the prevalence of aminoglycoside-modifying enzyme (AME) genes, while investigating the coexistence of 16S rRNA methylating enzymes. We analyzed 628 clinical isolates of Enterobacterales obtained from 4282 cytobacteriological urine examinations at the Pasteur Institute Casablanca, Morocco, collected from October 2018 to December 2021. Identification and antibiotic susceptibility testing were conducted using the VITEK 2^® COMPACT system, following CA-SFM guidelines. DNA extraction utilized the heat shock method, and subsequent PCR was performed. Gram-negative bacteria accounted for 85% of isolates, with Enterobacterales representing 91% of this group. E. coli (73%) and Klebsiella pneumoniae (20%) were the most common species among Enterobacterales. Resistance was particularly high for ampicillin (76.7%) and amoxicillin-clavulanate (58%). Among aminoglycosides, gentamicin and tobramycin resistance rates were 33.5% and 35%, respectively, while amikacin resistance was observed in 21.3% of isolates. High frequencies of AME genes were detected, with AAC(3′)-IIa (27.7%) and AAC(6′)-Ib (25.9%) being the most prevalent. Notably, no 16S rRNA methylation genes (rmtA, rmtB, rmtC, rmtD) were found. All tested strains exhibited biofilm-forming capacity, with K. pneumoniae demonstrating intense biofilm production. The study highlights a concerning trend of antibiotic resistance among uropathogenic Enterobacterales in the community setting, correlating genotype with resistance phenotype and emphasizing the need for enhanced surveillance and targeted treatment strategies. Full article

To begin formulating broiler diets on a digestible calcium (Ca) basis, robust Ca digestibility values for ingredients and factors affecting this digestibility are needed. This study had three main objectives: (1) determine the standardized ileal digestibility (SID) of Ca and phosphorus (P) for seven plant-based feed ingredients in broilers, (2) assess the impact of phytate source on SID Ca from limestone (LS), and (3) evaluate the effect of phytase on SID Ca and P for the different ingredients. Two experiments were conducted to satisfy these objectives. In Experiment 1, a 4 × 2 × 2 factorial design was used, with four plant-based feed ingredients (corn, wheat, sorghum, and full-fat soybean meal (FFS)), two LS inclusions in the diet (absence of LS and the inclusion of LS required to achieve 0.65% Ca in the final diet), and two phytase doses (0 and 1000 FTU/kg diet). Experiment 2 utilized a 3 × 2 × 2 factorial design with three plant-based ingredients (soybean meal (SBM), rapeseed meal (RSM), and sunflower meal (SFM)), two LS inclusions in the diet (absence of LS and the inclusion of LS required to achieve 0.65% Ca in the final diet), and two phytase doses (0 and 1000 FTU/kg diet). The trial had eight replicate pens (6 broilers/replicate) per treatment. Data were analyzed using a factorial analysis in JMP Pro 16.0 with means separation performed when p < 0.05, using Tukey HSD. The SID Ca in the absence of phytase for wheat (72.9%) and FFS (69.9%) was higher (p < 0.05) than for sorghum (54.5%) and corn (46.3%). In Experiment 2, the SID Ca in the absence of phytase from SFM (61.0%) was higher (p < 0.01) than RSM (42.7%) and SBM (46.8%). The SID Ca from added LS was affected by the ingredient, with diets containing wheat and FFS resulting in the lowest (p < 0.05) SID Ca versus those containing corn and sorghum irrespective of phytase dose in Experiment 1, and the lowest (p < 0.05) for SBM and RSM vs. SFM in the absence of phytase in Experiment 2. Phytase supplementation increased (p < 0.01) SID Ca and SID P across all feed ingredients compared to non-supplemented diets. There was a two-way interaction (p < 0.01) of LS addition and ingredient on SID P in both experiments. The results of this study provide SID Ca and SID P values from the selected ingredients and show that phytate from different ingredients reacts differently with Ca from LS and should be considered when developing SID coefficients of Ca and P for use in commercial broiler feed formulation. The SID coefficients of Ca and P for the individual feed ingredients evaluated in this study will allow for the further development and transition towards dCa and dP in commercial feed formulation. Full article

Ensuring the sustainability of forests is among the priority measures to be taken against the decline in biodiversity, which is among the world’s increasingly common concerns. This study investigated whether sustainable forest management processes are considering wildlife conservation objectives. Ten forest management processes were categorized and then analyzed for whether wildlife conservation is adequately considered. The wildlife data were grouped into four categories, with the most common being the protection of biodiversity and wildlife trade. The satisfaction level obtained according to the scoring method used was determined as the criterion of success in wildlife conservation. According to the scoring method applied, the overall success was found to be 50%. It was determined that a standard should be developed regarding the economic value of wildlife fauna and flora species and that this issue should be included in sustainable forest management strategies. Only 20 of 116 total sustainable forest management criteria considered wildlife. The African Timber Organization process, which has the most member countries, was identified as the process with the lowest number of wildlife criteria, at 2%, while the International Tropical Timber Organization process was found to have the most wildlife protection criteria at 20%. The conservation success rates for the two processes of which Türkiye is a member were also found to be quite low. It is concluded that there is a need to strengthen the place of wildlife, one of the most important living components for forests, in SFM processes both for Türkiye and internationally. The results obtained were evaluated both in terms of international criteria and practices in Türkiye. It is also recommended that future international meetings include wildlife health and diversity as a separate criterion when determining sustainable methods. Full article

Situated throughout the southeastern United States within the Laurentian craton are occurrences of various aged deposits (Late Proterozoic to Early Paleogene) that contain volcanics spanning from lamprophyres to carbonatites and basalts to rhyolites. Several are intrusive, while others have been reworked detritally, deposited as river gravels out onto the Gulf Coastal Plain. The earliest occurrence of igneous gravel clasts in the coastal plain of the lower Mississippi Valley lie along the Mississippi River’s eastern valley wall in the ancestral Mississippi River’s pre-loess terrace deposits (PLTDs). The coarse clastics of the PLTDs are dominantly chert gravels derived from Paleozoic carbonate bedrock, but also include clasts of Precambrian Sioux Quartzite, glacially faceted and striated stones, and ice-rafted boulders, which indicate a direct relationship between the PLTDs and glacial outwash during the cyclic glaciation of the Pleistocene Epoch. The PLTDs also contain the oldest known examples of igneous gravels exposed at the surface in Mississippi. An understanding of their igneous bedrock provenance and the timing of their contribution to the sedimentary record of the lower Mississippi River Valley sheds a valuable light onto the geologic history and evolution of the ancestral Mississippi River during the Pleistocene Epoch. The use of fusion inductively coupled plasma mass-spectroscopy (ICP-MS) in the identification of the igneous suites of one of the pre-loess terraces, well-delineated by geologic mapping, adds important geochemical source data from the gravel constituents for the further interpretation and correlation of the individual PLTD allounits. Gravel constituent geochemistry also offers a better understanding of the evolution of the ancestral Mississippi River watershed and the contributions of bedrock sources during Pleistocene glaciation. This petrological study suggests that the igneous gravels sampled from within the Rawhide PLTD allounit originated from the St. Francois Mountains (SFMs) in southwestern Missouri, with the implications that the SFM igneous terrain was in the direct path of the Independence “Kansan” glaciation. This could indicate a glacial extent further southwest than previously documented. Full article

The growing demand for high-accuracy mapping and 3D modeling using unmanned aerial vehicles (UAVs) has accelerated advancements in flight dynamics, positioning accuracy, and imaging technology. Structure from motion (SfM), a computer vision-based approach, is increasingly replacing traditional photogrammetry through facilitating the automation of processes such as aerial triangulation (AT), terrain modeling, and orthomosaic generation. This study examines methods to enhance the accuracy of SfM-based AT through real-time kinematic (RTK) UAV imagery, focusing on large-scale infrastructure applications, including a dam and its entire basin. The target area, primarily consisting of homogeneous water surfaces, poses considerable challenges for feature point extraction and image matching, which are crucial for effective SfM. To overcome these challenges and improve the AT accuracy, a constraint equation was applied, incorporating weighted 3D coordinates derived from RTK UAV data. Furthermore, oblique images were combined with nadir images to stabilize AT, and confidence-based filtering was applied to point clouds to enhance geometric quality. The results indicate that assigning appropriate weights to 3D coordinates and incorporating oblique imagery significantly improve the AT accuracy. This approach presents promising advancements for RTK UAV-based AT in SfM-challenging, large-scale environments, thus supporting more efficient and precise mapping applications. Full article

Forest degradation and forest disturbance are distinct yet often conflated concepts, complicating their definition and monitoring. Forest degradation involves interrupted succession and a severe reduction in forest services over time, caused by factors like fires, illegal selective logging, and edge effects. Forest disturbance, on the other hand, refers to abrupt, localized events, natural or anthropogenic, such as legal selective logging, tropical blowdowns, storms, or fires, without necessarily leading to long-term degradation. Despite the varying intensity and scale of forest degradation and disturbance, systematic studies distinguishing its types and classes are limited. This study reviews anthropogenic impacts on forests in the Brazilian Amazon, analyzing 80 scientific articles using remote sensing techniques and data. Most research focuses on the “arc of deforestation,” characterized by intense human activity, showcasing methodological advancements but also revealing gaps in monitoring less-studied regions like the central and western Amazon. The findings emphasize the need for advanced remote sensing tools to differentiate degradation types, particularly in sustainable forest management (SFM) contexts. Expanding research to underrepresented regions and refining methodologies are crucial for better understanding forest dynamics and improving conservation strategies. These efforts are essential to support effective forest management and informed policy development across the Amazon. Full article

The work developed aims to present an innovative methodology to execute the heritage conservation processes in a collaborative and interdisciplinary Building Information Modeling (BIM) project, with an effective management of the deterioration suffered over time, emphasizing the structures and coatings. The research begins with an architectural survey using terrestrial laser scanning (TLS) and terrestrial photogrammetry software, Structure from Motion (SfM), studying study the Duomo of Molfetta (Italy), a unique Romanesque architecture of Puglia (Italy). The methodological process is mainly aided by the precise semantic segmentation of global point clouds, a semi-automatic process assisted by classification algorithms implemented in the Cyclone 3DR post-processing software, which has allowed the classification of the unstructured information provided by the remote sensing equipment when identifying the architectural-structural systems of a building with high historical values. Subsequently, it was possible to develop an efficient Scan-to-HBIM workflow, where the Heritage BIM (HBIM) project has fulfilled the function of a database by incorporating and organizing all the information (graphic and non-graphic) to optimize the tasks of auscultation, identification, classification, and quantification and, in turn, facilitating the parametric modeling of unique structures and architectural elements. The results have shown great effectiveness in the processes of characterization of architectural heritage, focusing on the deformations and deterioration of the masonry in columns and pilasters. To make multidisciplinary conservation work more flexible, specific properties have been created for the identification and analysis of the degradation detected in the structures, with the HBIM project constituting a manager of the control and inspection activities. The restoration technician interacts with the determined 3D element to mark the “type decay”, managing the properties in the element’s own definition window. Interactive schemes have been defined that incorporate the items for the mapping of the elements, as well as particular properties of a conservation process (intervention, control, and maintenance). All listed parametric elements have links to be viewed in 2D and 3D views. Therefore, the procedure has facilitated the auscultation of the scanned element as it is semantically delimited, the parametric modeling of it, the analytical study of its materials and deterioration, and the association of intrinsic parameters so that they can be evaluated by all the intervening agents. But there are still some difficulties for the automatic interpretation of 3D point cloud data, related to specific systems of the historical architecture. In conclusion, human action and interpretation continues to be a fundamental pillar to achieve precise results in a heritage environment. Full article

Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. Alternatively, unmanned aerial vehicles (UAVs) can cover large areas while keeping operators safe from hazards including steep terrain. Despite their utility, optimal flight parameters to ensure flight efficiency and data quality remain under-researched. This study evaluated the impact of forward and side overlap and flight altitude on the quality of two- and three-dimensional spatial data products from UAV photogrammetry (UAV-SfM) for assessing stand density in a recently thinned Pinus radiata D. Don plantation. A contemporaneously acquired UAV laser scanner (ULS) point cloud provided reference data. The results indicate that the optimal UAV-SfM flight parameters are 90% forward and 85% side overlap at a 120 m altitude. Flights at an 80 m altitude offered marginal resolution improvement (2.2 cm compared to 3.2 cm ground sample distance/GSD) but took longer and were more error-prone. Individual tree detection (ITD) for stand density assessment was then applied to both UAV-SfM and ULS canopy height models (CHMs). Manual cleaning of the detected ULS tree peaks provided ground truth for both methods. UAV-SfM had a lower recall (0.85 vs. 0.94) but a higher precision (0.97 vs. 0.95) compared to ULS. Overall, the F-score indicated no significant difference between a prosumer-grade photogrammetric UAV and an industrial-grade ULS for stand density assessments, demonstrating the efficacy of affordable, off-the-shelf UAV technology for forest managers. Furthermore, in addressing the knowledge gap regarding optimal UAV flight parameters for conducting operational forestry assessments, this study provides valuable insights into the importance of side overlap for orthomosaic quality in forest environments. Full article

Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available phenotype information. To address the current challenge of acquiring sufficient non-destructive information from living potted plants, we proposed a three dimensional (3D) phenotyping pipeline that combines neural radiation field reconstruction with path analysis. An indoor collection system was constructed to obtain multi-view image sequences of potted plants. The structure from motion and neural radiance fields (SFM-NeRF) algorithm was then utilized to reconstruct 3D point clouds, which were subsequently denoised and calibrated. Geometric-feature-based path analysis was employed to separate stems from leaves, and density clustering methods were applied to segment the canopy leaves. Phenotypic parameters of potted plant organs were extracted, including height, stem thickness, leaf length, leaf width, and leaf area, and they were manually measured to obtain the true values. The results showed that the coefficient of determination (R²) values, indicating the correlation between the model traits and the true traits, ranged from 0.89 to 0.98, indicating a strong correlation. The reconstruction quality was good. Additionally, 22 potted plants were selected for exploratory experiments. The results indicated that the method was capable of reconstructing plants of various varieties, and the experiments identified key conditions essential for successful reconstruction. In summary, this study developed a low-cost and robust 3D phenotyping pipeline for the phenotype analysis of potted plants. This proposed pipeline not only meets daily production requirements but also advances the field of phenotype calculation for potted plants. Full article

To better address the challenges of complex backgrounds, varying object sizes, and arbitrary orientations in remote sensing object detection tasks, this paper proposes a deformable split fusion method based on an improved RoI Transformer called RoI Transformer-DSF. Specifically, the deformable split fusion method contains a deformable split module (DSM) and a space fusion module (SFM). Firstly, the DSM aims to assign different receptive fields according to the size of the remote sensing object and focus the feature attention on the remote sensing object to capture richer semantic and contextual information. Secondly, the SFM can highlight the spatial location of the remote sensing object and fuse spatial information of different scales to improve the detection ability of the algorithm for objects of different sizes. In addition, this paper presents the ResNext_Feature Calculation_block (ResNext_FC_block) to build the backbone of the algorithm and modifies the original regression loss to the KFIoU to improve the feature extraction capability and regression accuracy of the algorithm. Experiments show that the mAP_0.5 of this method on DOTAv1.0 and FAIR1M (plane) datasets is 83.53% and 44.14%, respectively, which is 3% and 1.87% higher than that of the RoI Transformer, and it can be applied to the field of remote sensing object detection. Full article