Modeling Of Trees Research Articles

ALS-Based, Automated, Single-Tree 3D Reconstruction and Parameter Extraction Modeling

The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling of trees by adding a quantitative analysis capability to acquire stand factors. We used unmanned aircraft LiDAR (ALS) data as the raw data for this study. After denoising the data and segmenting the single trees, we obtained the single-tree samples needed for this study and produced our own single-tree sample dataset. The scanned tree point cloud was reconstructed in three dimensions in terms of geometry and topology, and important stand parameters in forestry were extracted. This improvement in the quantification of model parameters significantly improves the utility of the original point cloud tree reconstruction algorithm and increases its ability for quantitative analysis. The tree parameters obtained by this improved model were validated on 82 camphor pine trees sampled from the Northeast Forestry University forest. In a controlled experiment with the same field-measured parameters, the root mean square errors (RMSEs) and coefficients of determination (R2s) for diameters at breast height (DBHs) and crown widths (CWs) were 4.1 cm and 0.63, and 0.61 m and 0.74, and the RMSEs and coefficients of determination (R2s) for heights at tree height (THs) and crown base heights (CBHs) were 0.55 m and 0.85, and 1.02 m and 0.88, respectively. The overall effect of the canopy volume extracted based on the alpha shape is closest to the original point cloud and best estimated when alpha = 0.3.

Modeling Realistic Geometries in Human Intrathoracic Airways.

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.

Interactive Invigoration: Volumetric Modeling of Trees with Strands

Generating realistic models of trees and plants is a complex problem because of the vast variety of shapes trees can form. Procedural modeling algorithms are popular for defining branching structures and steadily increasing their expressive power by considering more biological findings. Most existing methods focus on defining the branching structure of trees based on skeletal graphs, while the surface mesh of branches is most commonly defined as simple cylinders. One critical open problem is defining and controlling the complex details observed in real trees. This paper aims to advance tree modeling by proposing a strand-based volumetric representation for tree models. Strands are fixed-size volumetric pipes that define the branching structure. By leveraging strands, our approach captures the lateral development of trees. We combine the strands with a novel branch development formulation that allows us to locally inject vigor and reshape the tree model. Moreover, we define a set of editing operators for tree primary and lateral development that enables users to interactively generate complex tree models with unprecedented detail with minimal effort.

A 3D-Based Modeling and Analysis of an Artificial Palm Tree for Video Surveillance Activities

A study on the 3-D modeling and analysis of an artificial palm tree structure capable of housing a camera was made. The proposed system's design, simulation, and virtual testing were done using SolidWorks software. This work's primary objective was an aesthetically pleasing and functional solution that integrates with the urban landscape while providing an enhanced concealed security system. The natural form of a palm tree was incorporated into the artificial palm tree structure. The hidden camera ensures optimal coverage without compromising the overall appearance and presence of the device. This is so because, in the Nigerian situation, Miscreants will pull down the structure if they know of its presence. A detailed analysis of the system's structural integrity and stability was achieved using a 3D model of the artificial palm tree structure. Factors such as wind load, weight distribution, and material strength are considered to ensure the design can cope with environmental conditions. Virtual simulations allow for the maximization of the structure, ensuring it complies with safety standards, and the results of VonMise's stress show that the materialsselected for the design are suitable for the work.

Museum Skins Enable Identification of Introgression Associated with Cytonuclear Discordance.

Increased sampling of genomes and populations across closely related species has revealed that levels of genetic exchange during and after speciation are higher than previously thought. One obvious manifestation of such exchange is strong cytonuclear discordance, where the divergence in mitochondrial DNA (mtDNA) differs from that for nuclear genes more (or less) than expected from differences between mtDNA and nuclear DNA (nDNA) in population size and mutation rate. Given genome-scale data sets and coalescent modeling, we can now confidently identify cases of strong discordance and test specifically for historical or recent introgression as the cause. Using population sampling, combining exon capture data from historical museum specimens and recently collected tissues we showcase how genomic tools can resolve complex evolutionary histories in the brachyotis group of rock-wallabies (Petrogale). In particular, applying population and phylogenomic approaches we can assess the role of demographic processes in driving complex evolutionary patterns and assess a role of ancient introgression and hybridization. We find that described species are well supported as monophyletic taxa for nDNA genes, but not for mtDNA, with cytonuclear discordance involving at least 4 operational taxonomic units across 4 species which diverged 183-278 kya. ABC modeling of nDNA gene trees supports introgression during or after speciation for some taxon pairs with cytonuclear discordance. Given substantial differences in body size between the species involved, this evidence for gene flow is surprising. Heterogenous patterns of introgression were identified but do not appear to be associated with chromosome differences between species. These and previous results suggest that dynamic past climates across the monsoonal tropics could have promoted reticulation among related species.

Vibration response characteristics of Jujube trees based on finite element method and structure-from-motion

It is an indispensable step to choose optimal vibration parameters in jujube fruits vibration harvesting. Excessive vibration frequency will cause jujube tree damage, otherwise, the separation of fruit and fruit stalk cannot be achieved, resulting in harvesting failure. In our work, finite element method and vibration tests are applied to find optimal vibration parameters. To improve the accuracy of finite element method, a low-cost 3D reconstruction method is proposed to build jujube tree model. Experimental result showed that the phenotypic information of the reconstructed jujube tree and the real jujube tree have a high similarity, and their determination coefficient R2 reach to 0.984. Moreover, the natural frequencies of jujube tree are analyzed by modal analysis, transient analysis is applied to analyze vibration transfer characteristics. The simulation and experimental results show that the optimal vibration frequency range of jujube tree is 25∼30 Hz, the optimal vibration excitation location is on the main trunk and as high as possible close to the secondary branches, which can obtain maximum vibration response for optimum harvesting efficiency. The average determination coefficient is 0.66 between test and simulation results at different frequencies. This study can provide references for accurate 3D modeling of jujube trees, selection of appropriate vibration parameter range and the development of jujube harvesting machinery.

Stochastic and self-consistent 3D modeling of streamer discharge trees with Kinetic Monte Carlo

This paper contains the foundation for a new Particle-In-Cell model for gas discharges, based on Îto diffusion and Kinetic Monte Carlo (KMC). In the new model the electrons are described with a microscopic drift-diffusion model rather than a macroscopic one. We discuss the connection of the Îto-KMC model to the equations of fluctuating hydrodynamics and the advection-diffusion-reaction equation which is conventionally used for simulating streamer discharges. The new model is coupled to a particle description of photoionization, providing a non-kinetic all-particle method with several attractive properties, such as: 1) Taking the same input as a fluid model, e.g. mobility coefficients, diffusion coefficients, and reaction rates. 2) Guaranteed non-negative densities. 3) Intrinsic support for reactive and diffusive fluctuations. 4) Exceptional stability properties. The model is implemented as a particle-mesh model on cut-cell grids with Cartesian adaptive mesh refinement. Positive streamer discharges in atmospheric air are considered as the primary application example, and we demonstrate that we can self-consistently simulate large discharge trees.

Modeling the Geometry of Tree Trunks Using LiDAR Data

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

A Comparative Analysis of UAV Photogrammetric Software Performance for Forest 3D Modeling: A Case Study Using AgiSoft Photoscan, PIX4DMapper, and DJI Terra.

Three-dimensional (3D) modeling of trees has many applications in various areas, such as forest and urban planning, forest health monitoring, and carbon sequestration, to name a few. Unmanned Aerial Vehicle (UAV) photogrammetry has recently emerged as a low cost, rapid, and accurate method for 3D modeling of urban and forest trees replacing the costly traditional methods such as plot measurements and surveying. There are numerous commercial and open-source software programs available, each processing UAV data differently to generate forest 3D modeling and photogrammetric products, including point clouds, Digital Surface Models (DSMs), Canopy Height Models (CHMs), and orthophotos in forest areas. The objective of this study is to compare the three widely-used commercial software packages, namely, AgiSoft Photoscan (Metashape) V 1.7.3, PIX4DMapper (Pix4D) V 4.4.12, and DJI Terra V 3.7.6 for processing UAV data over forest areas from three perspectives: point cloud density and reconstruction quality, computational time, DSM assessment for height accuracy (z) and ability of tree detection on DSM. Three datasets, captured by UAVs on the same day at three different flight altitudes, were used in this study. The first, second, and third datasets were collected at altitudes of 60 m, 100 m, and 120 m, respectively over a forested area in Tully, New York. While the first and third datasets were taken horizontally, the second dataset was taken 20 degrees off-nadir to investigate the impact of oblique images. Results show that Pix4D and AgiSoft generate 2.5 times denser point clouds than DJI Terra. However, reconstruction quality evaluation using the Iterative Closest Point method (ICP) shows DJI Terra has fewer gaps in the point cloud and performed better than AgiSoft and Pix4D in generating a point cloud of trees, power lines and poles despite producing a fewer number of points. In other words, the outperformance in key points detection and an improved matching algorithm are key factors in generating improved final products. The computational time comparison demonstrates that the processing time for AgiSoft and DJI Terra is roughly half that of Pix4D. Furthermore, DSM elevation profiles demonstrate that the estimated height variations between the three software range from 0.5 m to 2.5 m. DJI Terra's estimated heights are generally greater than those of AgiSoft and Pix4D. Furthermore, DJI Terra outperforms AgiSoft and Pix4D for modeling the height contour of trees, buildings, and power lines and poles, followed by AgiSoft and Pix4D. Finally, in terms of the ability of tree detection, DJI Terra outperforms AgiSoft and Pix4D in generating a comprehensive DSM as a result of fewer gaps in the point cloud. Consequently, it stands out as the preferred choice for tree detection applications. The results of this paper can help 3D model users to have confidence in the reliability of the generated 3D models by comprehending the accuracy of the employed software.

A Study of Measurement Modeling of Decision Trees in Machine Learning Processes

Abstract Accompanied by the rapid development of economy and science and technology, the ordinary measurement model with a single method of parameter determination and accuracy is not guaranteed, which has made it difficult to adapt to the measurement needs of complex data in industrial engineering and other systems. This study proposes a measurement model for complex data through the optimization of decision trees in the process of machine learning. Firstly, the gradient-boosting-based decision tree measurement model (GBDT) is constructed by analyzing the decision tree model, and then the model is solved. At the same time, latent variables were included in the model, SEM described the reflection relationship of explicit variables to latent variables, and the GBDT optimization model, including latent variables, was constructed by using the results of the model measurement, including latent variables. Then, for the measurement of multivariate data, the fusion convolutional network was used for image data feature extraction, and the combined measurement model with multi-source data fusion (MDF-DTFEE) was constructed on the basis of the decision tree measurement model. In the empirical analysis of the measurement model, the predicted and actual values of the model training were fitted between 4~60 mg/L and 5~45 ml/L, respectively, and its R² on the training set and test set were 0.948 and 0.886, respectively, with the RMSE lower than 1.2, and none of the MAPE exceeded 0.2. The practical application always had an error range of 1 mg/L, which is in line with the requirements. It fulfills the practical application requirements, demonstrates the practical value of the measurement model in this paper, and provides a useful solution for measuring complex data.

Potential species for high biomass production and allometric modelling of even-aged native tropical lowland trees of Indonesia

Potential species for high biomass production and allometric modelling of even-aged native tropical lowland trees of Indonesia

Modeling of the 3D Tree Skeleton using Real-World Data: A Survey.

Tree modeling has been extensively studied in computer graphics. Recent advances in the development of high-resolution sensors and data processing techniques are extremely useful for collecting 3D datasets of real-world trees and generating increasingly plausible branching structures. The rapid development of versatile acquisition platforms allows us to capture multi-view images and scanned data that can be used for guided 3D tree modeling. In this paper, we carry out a comprehensive review of the state-of-the-art methods for the 3D modeling of botanical tree geometry by taking input data from real scenarios. A wide range of studies has been proposed following different approaches. To summarize the most relevant contributions, these have been classified into three categories: (1) procedural reconstruction, (2) geometry-based extraction, which is also divided into three groups (thinning, clustering and spanning tree refinement), and (3) image-based modeling. In addition, we describe other approaches aimed at improving the reconstruction process by adding additional features to achieve a realistic appearance of the tree models. Thus, we provide an overview of the most effective procedures to assist researchers in the photorealistic modeling of trees in geometry and appearance. The article concludes with remarks and trends for promising research opportunities in 3D tree modeling using real-world data.

Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling

ABSTRACT Three-dimensional models of trees that correspond to the real-world forms of the trees on the ground are used in urban planning, solar power estimation, and other disciplines. Previous studies have focused on generating 3D tree models from high-density point cloud data such as Terrestrial Laser Scanning (TLS) data, which is expensive and limited to small spatial extents. However, there has been limited exploration of inexpensive solutions to model trees over large spatial extents. The goal of this study is to use widely available discrete return Airborne Laser Scanning (ALS) data along with field-captured tree photographs and Google Street View (GSV) images to develop 3D equivalents of trees over larger spatial extents. To this end, we designed a process to assign representative 3D models for individual trees in discrete return ALS point clouds. This study demonstrates the use of a Convolutional Neural Network (CNN) model and 3D models generated with Structure from Motion (SfM) for the realistic modelling of deciduous non-overlapping trees from discrete return ALS data. We classified and labelled the crown shapes of deciduous trees in a study area into four classes based on GSV images of trees. We delineated and segmented non-overlapping deciduous trees from ALS data and reduced them to 2D images using voxel point counts. Next, we trained a CNN architecture to match the 2D images to the corresponding classes observed from GSV images. For each class, we created a representative 3D tree model using field-captured circumnavigational photos of trees and SfM. To demonstrate 3D visualization using the 3D tree models, we created a 3D visualization of the trees surrounding a parking lot. The trained CNN model from this study can be used to classify non-overlapping deciduous trees from discrete return ALS data and subsequently visualize near-realistic 3D tree models of trees.

On the flow conditions requiring detailed geometric modeling for multiscale evaluation of coastal forests

The multiscale evaluation method is applied to assess the influence of detailed geometric modeling of trees on their macroscopic attenuation effect against tsunami-like flow. Specifically, we conduct a series of numerical flow tests (NFTs), i.e., 3D flow simulations in a local test domain (LTD), under various inflow conditions to evaluate the macroscopic flow characteristics in the LTD accommodating an array of either simple cylinder or detailed tree models that mimic a coastal forest. After the procedure of NFTs in the multiscale evaluation method is briefly summarized and the corresponding governing equations and analysis conditions are presented, we introduce two indices for evaluating the macroscopic flow characteristics within the framework of multiscale modeling. Based on the NFT results, we discuss how the modeling scheme for trees influences the macroscopic flow characteristics in terms of these indices and clarify the microscopic mechanisms that influence the macroscopic attenuation property. Additionally, the NFT results are compared with the experimental results to justify these discussions, and key factors are explored in terms of reproducing real phenomena. In addition, to apply the multiscale evaluation method to assess the disaster mitigation performance of various types of ecosystems in a realistic situation, we discuss under what circumstances and with what level of detail vegetation should be modeled.

Automatic reconstruction and modeling of dormant jujube trees using three-view image constraints for intelligent pruning applications

Automatic reconstruction and modeling of dormant jujube trees provide a basis and prerequisite for intelligent pruning applications in large-scale, dwarf, and high-density planting orchards. For accurate 3D reconstruction of the trunk and pruning branches, this study presents a method based on a multi-view image sequence of dormant jujube trees. It involves four main steps. First, we determine the way of image acquisition, equipment parameters and posture, and reconstruction accuracy by investigating and analyzing their planting mode to meet the requirements of robot intelligent pruning operation. Second, performing image matching in images of dormant jujube trees is challenging due to few local feature regions, weak continuity, and difference in individual morphological structures. We provide a bilateral image-matching method based on the three-view geometry constraint. Third, this study optimizes a camera self-calibration algorithm based on image EXIF tags by broadening its applicability to improve reconstruction efficiency. It does not rely on the particular geometric constraints in the specific scenes. Finally, a dense 3D point cloud of a tree is built automatically with this improved SfM reconstruction strategy and combined with the PMVS algorithm. Both qualitative and quantitative evaluations are performed on generating 3D models of eight dormant jujube trees and their number of branches, average plant height, and crown are used as indicators. The results show that our method identifies the number of pruning branches with an accuracy of 92% and provides a performance of 95% for measuring the diameter of the plant height and canopy. The results provide visual research basis and technical support for the intelligent pruning of jujube trees.

Analysis of the Asymmetry between Both Eyes in Early Diagnosis of Glaucoma Combining Features Extracted from Retinal Images and OCTs into Classification Models

This study aims to analyze the asymmetry between both eyes of the same patient for the early diagnosis of glaucoma. Two imaging modalities, retinal fundus images and optical coherence tomographies (OCTs), have been considered in order to compare their different capabilities for glaucoma detection. From retinal fundus images, the difference between cup/disc ratio and the width of the optic rim has been extracted. Analogously, the thickness of the retinal nerve fiber layer has been measured in spectral-domain optical coherence tomographies. These measurements have been considered as asymmetry characteristics between eyes in the modeling of decision trees and support vector machines for the classification of healthy and glaucoma patients. The main contribution of this work is indeed the use of different classification models with both imaging modalities to jointly exploit the strengths of each of these modalities for the same diagnostic purpose based on the asymmetry characteristics between the eyes of the patient. The results show that the optimized classification models provide better performance with OCT asymmetry features between both eyes (sensitivity 80.9%, specificity 88.2%, precision 66.7%, accuracy 86.5%) than with those extracted from retinographies, although a linear relationship has been found between certain asymmetry features extracted from both imaging modalities. Therefore, the resulting performance of the models based on asymmetry features proves their ability to differentiate healthy from glaucoma patients using those metrics. Models trained from fundus characteristics are a useful option as a glaucoma screening method in the healthy population, although with lower performance than those trained from the thickness of the peripapillary retinal nerve fiber layer. In both imaging modalities, the asymmetry of morphological characteristics can be used as a glaucoma indicator, as detailed in this work.

Effect of Geometry Precision and Load Distribution on Branch Mechanical Response

Tree risk assessment requires mechanical response studies, but simplification of the shape, material, or boundary conditions is necessary when dealing with such complex structures. To observe overall tree response, sub-structuring to several levels of detail can be used, enabled by recent developments in numerical methods and three-dimensional laser scanning (3D scan). This study aimed to determine an appropriate level of geometry and loading simplification allowed for high-order branches at the crown border, which is useful for the mechanical analysis of structured tree models. Four higher-order branches were pruned and experimentally tested by single-point loading. Beam and solid finite-element models (FEMs) were created based on measured geometric parameters and detailed 3D scans, respectively. The FEMs were used to analyze seven loading scenarios with force applied at (a) the center of gravity, (b) the top of side branches, (c) key discrete points, and (d) uniformly to the whole volume (to each finite element). Force was distributed by ratios weighted according to the mass, area, and diameter of side branches; or according to the mass of each finite element. The results showed no significant difference between the beam model and 3D scan-based model. The scenarios with finite elements’ mass-based force distribution deviated significantly from those of the other scenarios. The most simplified single-point loading caused a deviation in the deflection curve. The deviation of single-point loading in the case of the bending moment was related to force distribution ratios given by the branches architecture. Therefore, such loading simplification is not considered always appropriate. Consistency between the bending moment and branch deflection provided a representative mechanical response, recommended for further modeling of trees by sub-structuring.

In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation.

In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high-resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random. The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation. The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization-based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non-intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented. The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high-resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law ), radii (median Murray deviation ), and smoothly curved, non-intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability=0.98 ± 0.01). This method facilitates the generation of large datasets of high-resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.

A Single-Tree Point Cloud Completion Approach of Feature Fusion for Agricultural Robots

With the continuous development of digital agriculture and intelligent forestry, the demand for three-dimensional modeling of trees or plants using agricultural robots is also increasing. Laser radar technology has gradually become an important technical means for agricultural robots to obtain three-dimensional information about trees. When using laser radar to scan trees, incomplete point cloud data are often obtained due to leaf occlusion, visual angle limitation, or operation error, which leads to quality degradation of the subsequent 3D modeling and quantitative analysis of trees. At present, a lot of research work has been carried out in the direction of point cloud completion, in which the deep learning model is the mainstream solution. However, the existing deep learning models have mainly been applied to urban scene completion or the point cloud completion of indoor regularized objects, and the research objects generally have obvious continuity and symmetry characteristics. There has been no relevant research on the point cloud completion method for objects with obvious individual morphological differences, such as trees. Therefore, this paper proposes a single-tree point cloud completion method based on feature fusion. This method uses PointNet, based on point structure, to extract the global features of trees, and EdgeConv, based on graph structure, to extract the local features of trees. After integrating global and local features, FoldingNet is used to realize the generation of a complete point cloud. Compared to other deep learning methods on the open source data set, the CD index using this method increased by 21.772% on average, and the EMD index increased by 15.672% on average, which proves the effectiveness of the method in this paper and provides a new solution for agricultural robots to obtain three-dimensional information about trees.

Applied Digital Documentation with Parametric 3D Modeling and Internet of Trees Functionality for Smart Forests and Monument Landscapes

The Problem: The classic traditional digital documentation (i.e. study, design, development, and maintenance of forests, parks, and monument landscapes) shows significant problems of functionality, adaptability, sustainability, velocity, and viability.
 Target & Research Objectives: In the framework of forests, parks, and monument landscapes, the massive volumes of Big Data (greater variety, arriving in increasing volumes, and with more velocity) can be used to address documentation problems that wouldn’t have been able to tackle before. Smart forest, as an Internet- enabled “product”, requires Big Data because it operates in real-time and requires real-time evaluation and action. Also, the recent research and practice advances in Blockchain data structures and Distributed Ledger Technologies (DLT) support generic structures with many services (e.g. parametric functionality). Hence, a DLT smart digital documentation can address the documentation problems.
 The research objectives, for digital documentation with blockchain functionality (DLT smart documentation), are defined by grouping in user-friendly palettes generic 3D CAD modeling tools which could be parameterized (e.g. trees modeling) in order to support adaptability; and by designing a new flexible and customized GUI (Graphical User Interface).
 Methods: The proposed methodology is based on parameterized 3D modeling of trees usually found in forests and urban parks (width and height parameters). Also, a personalized GUI operates as an interface between the end-user and the parameterized CAD (GUI palettes with many 3D modeling tools). Hence, in this paper, a parameterized 3D design is examined, analyzed, and presented in the context of digital documentation with internet of trees functionality for smart forests, urban parks, monument landscapes, and cultural heritage.
 Results: From the research conducted the results are: (i) a personalized, innovative, and flexible graphical interface (GUI) that could be incorporated in any commercial CAD environment; (ii) many parameterized 3D design tools specialized in the development of forms, plans, and modules (e.g., parametric tree 3D models) of objects and entities found in forests, urban parks, and monument landscapes; and (iii) the introduced internet of trees operativity (software routine), ideal for smart forests, smart monument landscapes, and landscape architecture digital documentation applications with blockchain functionality.
 Application: Possibility to support foresters, engineers, and landscape architects in development studies and documentation of peri-urban forests, recreational parks, pocket parks, monument landscapes, and cultural heritage projects. Increased integration functionality in blockchain knowledge databases.