Search Results (1,444)

Diplodia shoot blight is an opportunistic fungal pathogen infesting many conifer species and it has a global distribution. Depending on the duration and severity of the disease, affected needles appear yellow (chlorotic) for a brief period before becoming red or brown in colour. These symptoms can occur on individual branches or over the entire crown. Aerial sketch-mapping or the manual interpretation of aerial photography for tree health surveys are labour-intensive and subjective. Recently, however, the application of deep learning (DL) techniques to detect and classify tree crowns in high-spatial-resolution imagery has gained significant attention. This study evaluated two complementary DL approaches for the detection and classification of Pinus radiata trees infected with diplodia shoot blight across five geographically dispersed sites with varying topographies over two acquisition years: (1) object detection using YOLOv12 combined with Segment Anything Model (SAM) and (2) pixel-level semantic segmentation using U-Net, SegFormer, and EVitNet. The three damage classes for the object detection approach were ‘yellow’, ‘red-brown’ (both whole-crown discolouration) and ‘dead tops’ (partially discoloured crowns), while for the semantic segmentation the three classes were yellow, red-brown, and background. The YOLOv12m model achieved an overall mAP50 score of 0.766 and mAP50–95 of 0.447 across all three classes, with red-brown crowns demonstrating the highest detection accuracy (mAP50: 0.918, F1 score: 0.851). For semantic segmentation models, SegFormer showed the strongest performance (IoU of 0.662 for red-brown and 0.542 for yellow) but at the cost of longest training time, while EVitNet offered the most cost-effective solution achieving comparable accuracy to SegFormer but with a superior training efficiency with its lighter architecture. The accurate identification and symptom classification of crown damage symptoms support the calibration and validation of satellite-based monitoring systems and assist in the prioritisation of ground-based diagnosis or management interventions. Full article

Lateral Organ Boundaries Domain (LBD) proteins are plant-specific transcription factors characterized by a typical N-terminal LOB domain and are critical for plant growth, development, and stress response. Currently, LBD genes have been investigated in various plant species, but they have yet to be identified in Neolamarckia cadamba, known as a ‘miracle tree’ for its fast growth and acknowledged for its potential medicinal value in tropical and subtropical areas of Asia. In this study, a total of 65 NcLBD members were identified in N. cadamba by whole-genome bioinformatics analysis. Phylogenetic analysis revealed their classification into two clades with seven distinct groups, and their uneven distribution across 18 chromosomes, along with 6 tandem repeats and 58 segmental duplications. Furthermore, enrichment analysis of transcription factor binding motifs within NcLBD promoters identified the MYB-related and WRKY families exhibited the most significant enrichment in the NcLBD promoter. Protein interaction network analysis revealed potential interactions among NcLBD proteins, as well as their interactions with various transcription factors. RNA-seq and qRT-PCR analyses of NcLBDs transcript levels showed distinct expression patterns both across various tissues and under different hormone and abiotic stress conditions. Specifically, NcLBD3, NcLBD37, and NcLBD47 were highly expressed in vascular cells and induced by abiotic stress, including cold, drought, and salt, suggesting their significant role in the processes. In summary, our genome-wide analysis comprehensively identified and characterized LBD gene family in N. cadamba, laying a solid foundation for further elucidating the biological functions of NcLBD genes. Full article

Precise crown segmentation is essential for assessing structure, competition, and productivity in agroforestry systems, but delineation is challenging due to canopy heterogeneity and variability in aerial imagery. This study analyzes how flight height and orientation affect segmentation accuracy in an agroforestry system of the Peruvian Amazon, using RGB images acquired with a DJI Mavic Mini 3 Pro UAV and the instance-segmentation models YOLOv8 and YOLOv11. Four flight heights (40, 50, 60, and 70 m) and two orientations (parallel and transversal) were analyzed in an agroforestry system composed of Cedrelinga cateniformis (Ducke) Ducke, Calycophyllum spruceanum (Benth.) Hook.f. ex K.Schum., and Virola pavonis (A.DC.) A.C. Sm. Results showed that a flight height of 60 m provided the highest delineation accuracy (F1 ≈ 0.88 for YOLOv8 and 0.84 for YOLOv11), indicating an optimal balance between resolution and canopy coverage. Although YOLOv8 achieved the highest precision under optimal conditions, it exhibited greater variability with changes in flight geometry. In contrast, YOLOv11 showed a more stable and robust performance, with generalization gaps below 0.02, reflecting a stronger adaptability to different acquisition conditions. At the species level, vertical position and crown morphological differences (Such as symmetry, branching angle, and bifurcation level) directly influenced detection accuracy. Cedrelinga cateniformis displayed dominant and asymmetric crowns; Calycophyllum spruceanum had narrow, co-dominant crowns; and Virola pavonis exhibited symmetrical and intermediate crowns. These traits were associated with the detection and confusion patterns observed across the models, highlighting the importance of crown architecture in automated segmentation and the potential of UAVs combined with YOLO algorithms for the efficient monitoring of tropical agroforestry systems. Full article

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, and accurate coronary artery segmentation in X-ray coronary angiography (XCA) is challenged by low contrast, structural ambiguity, and anisotropic vessel trajectories, which hinder quantitative coronary angiography. We propose HR-UMamba++, a U-Mamba-based framework centered on a rotation-aligned multi-directional state-space scan for modeling long-range vessel continuity across multiple orientations. To preserve thin distal branches, the framework is equipped with (i) a persistent high-resolution bypass that injects undownsampled structural details and (ii) a UNet++-style dense decoder topology for cross-scale topological fusion. On an in-house dataset of 739 XCA images from 374 patients, HR-UMamba++ is evaluated using eight segmentation metrics, fractal-geometry descriptors, and multi-view expert scoring. Compared with U-Net, Attention U-Net, HRNet, U-Mamba, DeepLabv3+, and YOLO11-seg, HR-UMamba++ achieves the best performance (Dice 0.8706, IoU 0.7794, HD95 16.99), yielding a relative Dice improvement of 6.0% over U-Mamba and reducing the deviation in fractal dimension by up to 57% relative to U-Net. Expert evaluation across eight angiographic views yields a mean score of 4.24 ± 0.49/5 with high inter-rater agreement. These results indicate that HR-UMamba++ produces anatomically faithful coronary trees and clinically useful segmentations that can serve as robust structural priors for downstream quantitative coronary analysis. Full article

This study presents a geometry-based post-processing framework developed to refine deep-learning detections of tornado-damaged trees. The YOLO11-based instance segmentation framework served as the baseline, but its predictions often included multiple masks for a single tree or incomplete fragments of the same trunk, particularly in dense canopy areas or within tiled orthomosaics. Overlapping masks led to duplicated predictions of the same tree, while fragmentation broke a single fallen trunk into disconnected parts. Both issues reduced the accuracy of tree-count estimates and weakened orientation analysis, two factors that are critical for treefall methods. To resolve these problems, a Shape-Aware Non-Maximum Suppression (SA-NMS) procedure was introduced. The method evaluated each mask’s collinearity and, based on its geometric condition, decided whether segments should be merged, separated, or suppressed. A spatial assessment then aggregated prediction vectors within a defined Region of Interest (ROI), reconnecting trunks that were divided by obstacles or tile boundaries. The proposed method, applied to high-resolution orthomosaics from the December 2021 Land Between the Lakes tornado, achieved 76.4% and 77.1% instance-level orientation agreement accuracy in two validation zones. Full article

Accurate individual tree inventories are fundamental to urban forest management, yet automated delineation from Terrestrial Laser Scanning (TLS) data remains a challenge. This study presents a two-stage hybrid framework that combines a domain-adapted deep learning model (TreeLA-Net) with a geometric algorithm (SEGR) to solve this issue, aiming to reduce the need for instance-level annotations. TreeLA-Net first generates semantic labels, outperforming the baseline RandLA-Net by 2.5 percentage points in overall accuracy. Subsequently, SEGR leverages these priors to achieve a tree detection rate of 92.0% on our primary study site. To assess the framework’s transferability, an external validation was conducted on a new, independent site, where the model, without retraining, yielded a recall of 81.5%. These findings suggest that the framework is not strictly overfitted and possesses generalization capabilities. The proposed approach is offered as a potential tool to support data-driven urban forest management, particularly for automated tree mapping and inventory. We hope that this study may contribute to ongoing efforts to develop robust methods for characterizing complex urban forest structures. Full article

Forests are a critical component of global carbon sequestration, biodiversity, and ecosystem services, making accurate mapping essential for long-term monitoring. In North Korea, limited field access, rugged topography, and inconsistent national statistics necessitate reliable remote sensing–based observation. However, frequent cloud contamination challenges the use of optical time-series imagery for forest monitoring. This study introduces a framework that integrates a ConvLSTM-based autoencoder into a U-Net segmentation model to improve tree cover classification from Sentinel-2 time-series data. The autoencoder was pretrained to reconstruct cloud-contaminated or missing observations using multi-octave Perlin-noise perturbations, providing standardized inputs that enhanced segmentation robustness under noisy conditions. Results show that tree cover accuracy exceeded 96% when all five time steps were available and remained stable (94–95%) even with one missing step. Accuracy declined below 90% with three missing steps but remained above 80%, enabling draft classifications under limited data. Confidence analysis further indicated that model certainty is a practical quality-control metric. Annual mapping for 2019–2024 showed a general increase in tree cover, aligning with reported afforestation efforts in North Korea. Taken together, the framework advances long-term monitoring, carbon accounting, and risk assessment in North Korea, while also enabling robust, region-adapted monitoring in cloud-prone, data-limited settings. Full article

Vegetation phenology significantly influences urban microclimate and thermal comfort in cold regions, yet its quantitative impact—specifically the potential of deciduous trees to enhance winter solar access—remains underexplored. This study investigates how seasonal vegetation changes affect thermal conditions in an urban plaza. Field measurements were conducted at Beiyang Plaza, Tianjin University, during the autumn–winter transition. High-precision Sky View Factors (SVF) were extracted from panoramic images using a deep learning-based semantic segmentation model (PSPNet), validated against field observations. The Universal Thermal Climate Index (UTCI) was calculated to assess thermal stress. Results indicate that the leaf-off phase significantly increases SVF, shifting the radiative balance. Areas experiencing phenological changes exhibited a marked improvement in UTCI, effectively alleviating cold stress by maximizing solar gain. Advanced statistical models (ARIMAX and GAM) confirmed that, after controlling for background climatic variations, the positive effect of vegetation phenology on thermal comfort is statistically significant. These findings challenge the traditional focus on summer shading, highlighting the “winter-warming” potential of deciduous trees and providing quantitative evidence for climate-responsive urban design. Full article

Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective urban green space management. However, existing methods often struggle with noise, crown overlap, and the complexity of street environments. To address these challenges, this paper introduces a multi-constraint and shortest path optimization method for individual urban street tree segmentation from point clouds. In this paper, object primitives are first generated using multi-constraints based on graph segmentation. Subsequently, trunk points are identified and associated with their corresponding crowns through structural cues. To further improve the robustness of the proposed method under dense and cluttered conditions, the shortest-path optimization and stem-axis distance analysis techniques are proposed to further refine the individual tree extraction results. To evaluate the performance of the proposed method, the WHU-STree benchmark dataset is utilized for testing. Experimental results demonstrate that the proposed method achieves an average F1-score of 0.768 and coverage of 0.803, outperforming superpoint graph structure single-tree classification (SSSC) and nyström spectral clustering (NSC) methods by 17.4% and 43.0%, respectively. The comparison of visual individual tree segmentation results also indicates that the proposed framework offers a reliable solution for street tree detection in complex urban scenes and holds practical value for advancing smart city ecological management. Full article

Objective: The aim of this preliminary study was to recognize motion patterns by classifying time series features extracted from electromyography (EMG) data of the upper limb muscles. Methods: In this study, we tested six machine learning (ML) classification models (decision trees, support vector machines, linear discriminant, quadratic discriminant, k-nearest neighbors, and efficient logistic regression) to classify time series features segmented from processed EMG data that were acquired from eight superficial muscles of two upper limbs over performing given physical activities in two main stages (supination and neutral forearm configuration) in initial and target (isometric) positions. Results: Findings indicate that in aiming to classify stages of the upper limb with the highest performance, the following ML models should be used: (1) K-NN cityblock (F1 equals 0.973/0.992) and K-NN minkowski (0.966/0.992) for the left limb in initial or target position; (2) K-NN seuclidean (0.959/0.985) and K-NN minkowski (0.957/0.986) for the right limb in initial position; (3) K-NN cityblock (0.966/0.986), K-NN seuclidean (0.959/0.985), and K-NN minkowski (0.957/0.986) for the right limb in target position. Conclusions: Upper limb positions tested in this study can be recognized based on classification of surface EMG data by using the k-nearest neighbors models (K-NN cityblock, K-NN seuclidean or K-NN minkowski) that have to be trained separately for the right and left upper limbs. Full article

12-oxo-phytodienoic acid reductase (OPR) is a core component of the jasmonic acid (JA) biosynthetic pathway and participates in JA synthesis by catalyzing the reduction in the precursor 12-oxo-phytodienoic acid (OPDA), as well as broadly regulating plant development, stress response, and hormone signaling networks. This study analyzed the OPR gene family using 28 soybean genomes. A total of 15 OPR gene family members in soybean were identified, including 14 core genes and one variable gene. Analysis of gene duplication types showed that whole-genome duplication (WGD)/segmental duplication was the main mode of duplication in GmOPRs. The phylogenetic tree constructed from multiple species showed that the OPRs in subgroup VII were functionally important OPR genes and that the OPRs underwent Leguminosae and Cruciferae divergence, and large-scale duplication occurred in Leguminosae. Analysis of natural selection pressures on 28 soybean accessions indicated that the overall evolutionary pressures on GmOPRs were dominated by purifying selection, but there were also potential positive selection signals. Analysis of cis-acting elements revealed a large number of light- and hormone-responsive cis-acting elements in the GmOPRs. Some specific cis-acting elements were only present in a few genes or accessions. The protein interaction network consisted of 12 GmOPR proteins, 4 allene oxide synthase (AOS) proteins, and 6 allene oxide cyclase (AOC) proteins, where AOCs interact with GmOPRs and AOSs. Tissue transcriptome expression profiling showed that GmOPR3, GmOPR7, and GmOPR15 were specifically expressed in roots, whereas GmOPR2, GmOPR10, and GmOPR14 were specifically expressed in leaves, suggesting that these genes play an important role in the growth and development of the tissues. Moreover, GmOPRs usually responded to salt stress, and GmOPR3, GmOPR8, GmOPR9, GmOPR10, and GmOPR11 were significantly up-regulated in roots and leaves under salt stress. This suggests that these genes may be involved in biological processes such as osmoregulation, ion homeostasis, and scavenging of reactive oxygen species, thus helping soybeans to resist salt stress. This study comprehensively analyzed the OPR gene family in soybean based on the 28 soybean accessions and clarified the salt stress response pattern, which provides a new and more effective and reliable way to analyze the soybean gene family. Full article

The species, quantity, and tree diameter at breast height (DBH) are important indicators for assessing species distribution, individual growth status, and overall health in the forest. The existing tree information collection mainly relies on manual labor, which results in low efficiency and high labor intensity. To address these issues, we propose a method for tree species identification and diameter estimation by combining deep learning algorithms with binocular vision. First, an image acquisition platform is designed and integrated with a weeding machine to capture images during weeding operation. Images of seven types of trees are captured to develop a dataset. Second, a tree species identification model is established based on the YOLOv8n network, achieving 98.5% accuracy, 99.0% recall, and 99.2% mAP. Then, an improved YOLOv8n-seg model is proposed. It simplifies the network by introducing VanillaBlock in the backbone. FasterNet with a CCFM structure is added at the neck to enhance the model’s multi-scale expression capability. The mIoU of the improved model is 93.7%. Finally, the improved YOLOv8n-seg model is combined with binocular vision. After obtaining the segmentation mask of the tree, the spatial position of the two measurement points is calculated, allowing for the measurement of tree diameter. Verification experiments show that the average error for tree diameter ranges from 4.40~6.40 mm, and the proposed error compensation method can reduce diameter errors. This study provides a theoretical foundation and technical support for intelligent collection of tree information. Full article

Mediterranean dehesa ecosystems are highly valuable agroforestry systems from ecological, social and economic perspectives. Their structural characterization has traditionally relied on resource-intensive field inventories. This study assesses the applicability of low-density airborne LiDAR data from the Spanish National Aerial Orthophotography Plan (PNOA) for the automated morphological characterization of Quercus ilex dehesas. This novel workflow integrates the DBSCAN clustering algorithm for unsupervised segmentation of tree vegetation units and Concaveman for crown perimeter delineation and slicing using concave hulls. The technique was applied over 116 hectares in Santibáñez el Bajo (Cáceres), identifying 1254 vegetation units with 99.8% precision, 97.3% recall and an F-score of 98.5%. A field validation on 35 trees revealed strong agreement with the LiDAR-derived metrics, including crown diameter (R² = 0.985; bias = −0.96 m) and total height (R² = 0.955; bias = −0.34 m). Crown base height was overestimated (+0.77 m), leading to a 20.9% underestimation of crown volume, which was corrected using a regression model (R² = 0.952). This methodology allows us to produce scalable, fully automated forest inventories across extensive Iberian dehesas with similar structural characteristics using publicly available LiDAR data, even with a six-year acquisition gap. Full article

This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article