Search Results (86)

The crown area is a critical metric for evaluating tree growth and supporting various ecological and forestry analyses. This study compares three approaches, i.e., unsupervised clustering, region-based, and deep learning, to estimate the crown area of Pinus eldarica Medw. using UAV-acquired RGB imagery (2 cm ground sampling distance) and high-density point clouds (1.27 points/cm³). The first approach applied unsupervised clustering techniques, such as Mean-shift and K-means, to directly estimate crown areas, bypassing tree top detection. The second employed a region-based approach, using Template Matching and Local Maxima (LM) for tree top identification, followed by Marker-Controlled Watershed (MCW) and Seeded Region Growing for crown delineation. The third approach utilized a Convolutional Neural Network (CNN) that integrated Digital Surface Model layers with the Visible Atmospheric Resistance Index for enhanced segmentation. The results were compared against field measurements and manual digitization. The findings reveal that CNN and MCW with LM were the most effective, particularly for small and large trees, though performance decreased for medium-sized crowns. CNN provided the most accurate results overall, with a relative root mean square error (RRMSE) of 8.85%, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a bias score (BS) of 1.00. The CNN crown area estimates showed strong correlations (R² = 0.83, 0.62, and 0.94 for small, medium, and large trees, respectively) with manually digitized references. This study underscores the value of advanced CNN techniques for precise crown area and shape estimation, highlighting the need for future research to refine algorithms for improved handling of crown size variability. Full article

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management. Full article

Azadirachta indica was designated the “Tree of the 21st century” by the United Nations, as it is believed to be the largest natural depository of bioactive phytochemicals. This study investigates genetic variability among 152 Candidate Plus Trees (CPTs) of A. indica selected from three agro-climatic zones (ACZs) in eastern India: the Lower Gangetic Plains (ACZ III), Middle Gangetic Plains (ACZ IV), and the Eastern Plateau and Hills region (ACZ VII). Phenotypic characters, fruit and seed morphology, kernel oil content (KOC), and Azadirachtin concentration (AC) were assessed to characterize the genetic diversity. Significant variation was observed across all parameters among individual CPTs. Girth at breast height ranged from 0.9 to 2.8 m, tree height from 6 to 16 m, and crown volume from 146.95 to 2339.86 m³. Fruit length varied from 13.55 to 21.55 mm and seed length from 9.21 to 17.37 mm. KOC ranged from 36.51 to 58.86%, with a mean of 47.22% (±0.4), while AC showed extreme variability (19.46–1823.45 μg/g seed). KOC exhibited strong positive correlations with crown diameter (R = 0.57, p ≤ 0.001) and crown volume (R = 0.45, p ≤ 0.001). Interestingly, AC did not correlate significantly with any studied parameter. Analysis of variance revealed significant differences (p < 0.05) between ACZs, but only for some traits. All of the parameters demonstrated high heritability and moderate to high genetic advance. Cluster analysis using Ward’s minimum variance criterion based on Euclidean square (D²) distances performed in RStudio grouped the CPTs into five clusters as per pooled effects of all parameters. The highest inter-cluster distance was observed between Clusters III and V (7.703), indicating a potential for heterosis in hybridization between these groups. Each cluster contained CPTs from all three ACZs, suggesting uniformly distributed variation across the study area rather than zone-specific patterns. This study provides valuable insights for improvement programs of the species and emphasizes the need for further research, including progeny trials, to comprehensively understand the genetic variability of A. indica in eastern India. Full article

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

Roadside tree segmentation and parameter extraction play an essential role in completing the virtual simulation of road scenes. Point cloud data of roadside trees collected by LiDAR provide important data support for achieving assisted autonomous driving. Due to the interference from trees and other ground objects in street scenes caused by mobile laser scanning, there may be a small number of missing points in the roadside tree point cloud, which makes it familiar for under-segmentation and over-segmentation phenomena to occur in the roadside tree segmentation process. In addition, existing methods have difficulties in meeting measurement requirements for segmentation accuracy in the individual tree segmentation process. In response to the above issues, this paper proposes a roadside tree segmentation algorithm, which first completes the scene pre-segmentation through unsupervised clustering. Then, the over-segmentation and under-segmentation situations that occur during the segmentation process are processed and optimized through projection topology checking and tree adaptive voxel bound analysis. Finally, the overall high-precision segmentation of roadside trees is completed, and relevant parameters such as tree height, diameter at breast height, and crown area are extracted. At the same time, the proposed method was tested using roadside tree scenes. The experimental results show that our methods can effectively recognize all trees in the scene, with an average individual tree segmentation accuracy of 99.07%, and parameter extraction accuracy greater than 90%. Full article

Accurately understanding the stand composition of shelter forests is essential for the construction and benefit evaluation of shelter forest projects. This study explores classification methods for dominant tree species in shelter forests using UAV-derived RGB, hyperspectral, and LiDAR data. It also investigates the impact of individual tree crown (ITC) delineation accuracy, crown morphological parameters, and various data sources and classifiers. First, as a result of the overlap and complex structure of tree crowns in shelterbelt forests, existing ITC delineation methods often lead to over-segmentation or segmentation errors. To address this challenge, we propose a watershed and multi-feature-controlled spectral clustering (WMF-SCS) algorithm for ITC delineation based on UAV RGB and LiDAR data, which offers clearer and more reliable classification objects, features, and training data for tree species classification. Second, spectral, texture, structural, and crown morphological parameters were extracted using UAV hyperspectral and LiDAR data combined with ITC delineation results. Twenty-one classification images were constructed using RF, SVM, MLP, and SAMME for tree species classification. The results show that (1) the proposed WMF-SCS algorithm demonstrates significant performance in ITC delineation in complex mixed forest scenarios (Precision = 0.88, Recall = 0.87, F1-Score = 0.87), resulting in a 1.85% increase in overall classification accuracy; (2) the inclusion of crown morphological parameters derived from LiDAR data improves the overall accuracy of the random forest classifier by 5.82%; (3) compared to using LiDAR or hyperspectral data alone, the classification accuracy using multi-source data improves by an average of 7.94% and 7.52%, respectively; (4) the random forest classifier combined with multi-source data achieves the highest classification accuracy and consistency (OA = 90.70%, Kappa = 0.8747). Full article

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate ($A_r$) of 96.60%. Compared to the two conventional ITC delineation methods, the $A_r$ of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time. Full article

This study describes the first genome sequence and analysis of Coniella granati, a fungal pathogen with a broad host range, which is responsible for postharvest crown rot, shoot blight, and canker diseases in pomegranates. C. granati is a geographically widespread pathogen which has been reported across Europe, Asia, the Americas, and Africa. Our analysis revealed a 46.8 Mb genome with features characteristic of hemibiotrophic fungi. Approximately one third of its genome was compartmentalised within ‘AT-rich’ regions exhibiting a low GC content (30 to 45%). These regions primarily comprised transposable elements that are repeated at a high frequency and interspersed throughout the genome. Transcriptome-supported gene annotation of the C. granati genome revealed a streamlined proteome, mirroring similar observations in other pathogens with a latent phase. The genome encoded a relatively compact set of 9568 protein-coding genes with a remarkable 95% having assigned functional annotations. Despite this streamlined nature, a set of 40 cysteine-rich candidate secreted effector-like proteins (CSEPs) was predicted as well as a gene cluster involved in the synthesis of a pomegranate-associated toxin. These potential virulence factors were predominantly located near repeat-rich and AT-rich regions, suggesting that the pathogen evades host defences through Repeat-Induced Point mutation (RIP)-mediated pseudogenisation. Furthermore, 23 of these CSEPs exhibited homology to known effector and pathogenicity genes found in other hemibiotrophic pathogens. The study establishes a foundational resource for the study of the genetic makeup of C. granati, paving the way for future research on its pathogenicity mechanisms and the development of targeted control strategies to safeguard pomegranate production. Full article

Zirconia crowns are capping materials used in dentistry for tooth capping and are very popular due to their optical properties and natural-looking visuals. In vitro aging protocols measure zirconia’s optical properties, which are vital in order for it to look natural. This study aims to conduct a systematic review to explore the effect of in vitro aging protocols on the optical properties and crystalline structure of high-translucency (HT) zirconia. A correlation matrix was obtained using Microsoft Excel, which was later transferred into SPSS for confirmatory factor analysis (CFA) and hierarchal clustering and to obtain a dendrogram in order to display the distribution of clusters for each key term relevant to the study. Further, for qualitative analysis, 17 studies were screened and reviewed. The result demonstrates that high translucency has been observed in the crystalline structure of zirconia capping. However, quantitative and qualitative results did not demonstrate the in vitro protocol; instead, neglect of the in vitro protocol’s vitality in zirconia crown capping was alarming. Overall, zirconia has good optical properties when combined with catalysts such as aluminum and lithium to obtain a more sustainable crystalline structure. Full article

The frequent occurrence of localized and seasonal droughts has caused severe economic losses in maize production in South China. To promote sustainable maize production, selecting and breeding drought-tolerant varieties is vital for addressing water scarcity. Drought stress affects all aspects of crop morphological performance. In this study, the morphological performance of 285 maize inbred lines under drought stress was investigated using D-value analysis, correlation analysis, principal component analysis, cluster analysis and stepwise regression analysis. All indicators were significantly different in the regular treatment compared to the drought treatment. Specifically, survival rate, root fresh weight, root dry weight, plant dry weight, root/crown ratio, and plant fresh weight were used as indicators for drought-tolerance evaluation. Furthermore, the drought-tolerant inbred line CML323 and the drought-sensitive inbred line CB2-49-1 were screened by comprehensively evaluating D values. The drought-tolerant inbred line CML323 exhibits higher leaf relative water content, chlorophyll content, proline content, and ascorbate peroxidase and peroxidase activity while having lower malondialdehyde content, consequently demonstrating excellent drought tolerance. This study provides valuable insights into drought-tolerance indicators and reference materials for breeding maize varieties. Full article

Nine viruses of the bean common mosaic virus subgroup of potyviruses are major international crop pathogens, but their phylogenetically closest relatives from non-crop plants have mostly been found only in SE Asia and Oceania, which is thus likely to be their “centre of emergence”. We have compared over 700 of the complete genomic ORFs of the crop pandemic and the non-crop viruses in various ways. Only one-third of crop virus genomes are non-recombinant, but more than half the non-crop virus genomes are. Four of the viruses were from crops domesticated in the Old World (Africa to SE Asia), and the other five were from New World crops. There was a temporal signal in only three of the crop virus datasets, but it confirmed that the most recent common ancestors of all the crop viruses were before inter-continental marine trade started after 1492 CE, whereas all the crown clusters of the phylogenies are from after that date. The non-crop virus datasets are genetically more diverse than those of the crop viruses, and Tajima’s D analyses showed that their populations were contracting, and only one of the crop viruses had a significantly expanding population. dN/dS analyses showed that most of the genes and codons in all the viruses were under significant negative selection, and the few that were under significant positive selection were mostly in the PIPO-encoding region of the P3 protein, or the PIPO protein itself. Interestingly, more positively selected codons were found in non-crop than in crop viruses, and, as the hosts of the former were taxonomically more diverse than the latter, this may indicate that the positively selected codons are involved in host range determination; AlphaFold3 modelling was used to investigate this possibility. Full article

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R² values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management. Full article

In modern agricultural production, where a small number of commercial cultivars dominate, the collection, evaluation, and preservation of germplasm are important tasks to reduce the erosion of genes and preserve biodiversity. The aim of this study is to characterize the morphological and fruit chemical properties of the pomegranate germplasm grown on the East Adriatic coast, including the commercial cultivars ‘Hicaznar’, ‘Granada’, and ‘Wonderful’, and to highlight the characteristics with the greatest discriminating power. The characterization of the tree, leaf, flower, arils, seed, and juice was carried out using the UPOV descriptor. The colors of the peel, arils, and juice were analyzed according to the CIEL*a*b* method, total soluble solids were measured using refractometers, and total acidity was determined by titration with 0.1 M NaOH. The research results showed significant diversity between the cultivars, which were grouped into several clusters using an unsupervised analysis technique. Factors such as plant vigor, plant growth habit, predominant number of leaves per node on young shoots, crown type, fruit shape, fruit shape in cross-section, peel weight, total aril weight, aril weight, number of arils per fruit, seed length and width, seed yield, total acidity, TSS/TA ratio, and color parameters of the peel, arils, and juice showed high variability, indicating their strong discriminating power in determining the phenotypic diversity of pomegranate. Full article

Modern technologies, such as airborne laser scanning (ALS) and advanced data analysis algorithms, allow for the efficient and safe use of resources to protect infrastructure from potential threats. This publication presents a study to identify trees that may fall on highways. The study used free measurement data from airborne laser scanning and wind speed and direction data from the Institute of Meteorology and Water Management in Poland. Two methods were used to determine the crown tops of trees: PyCrown and OPALS. The effect of wind direction on potential hazards was then analyzed. The OPALS method showed the best performance in terms of detecting trees, with an accuracy of 74%. The analysis showed that the most common winds clustered between 260° and 290°. Potential threats, i.e., trees that could fall on the road, were selected. As a result of the analysis, OPALS detected between 140 and 577 trees, depending on the chosen strategy. The presented research shows that combining ALS technology with advanced algorithms and wind data can be an effective tool for identifying potential hazards associated with falling trees on highways. Full article

This study evaluated the salt tolerance in 40 celery germplasm resources to clarify the different salt tolerances of celery germplasm. A gradient treatment with different concentrations of NaCl solutions (100, 200, and 300 mmol·L⁻¹) was used to simulate salt stress. After 15 days of salt treatment, 14 indicators related to plant growth, physiology, and biochemistry were determined. The results showed that different celery varieties responded differently to salt stress. Notably, there were significant variations in below-ground dry weight, root–crown ratio, antioxidant enzyme activity, and soluble protein content among the accessions under salt stress. Principal component analysis was used to identify important indices for evaluating salt tolerance, including plant height, spread, content of soluble protein, and so on. A comprehensive evaluation was conducted utilizing the salt damage index, principal component analysis, affiliation function analysis, and cluster analysis. The 40 celery germplasms were classified into five highly salt-tolerant, seven salt-tolerant, fifteen moderately salt-tolerant, nine salt-sensitive, and four highly salt-sensitive germplasms. SHHXQ, MXKQ, XBQC, XQ, and TGCXBQ were highly salt-tolerant germplasms, and BFMSGQ, HNXQ, ZQ, and MGXQW were highly salt-sensitive germplasms. The results of this study provide a reference for the variety of celery cultivation in saline areas and lay a foundation for the selection and breeding of salt-tolerant varieties of celery. Full article