scholarly journals Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots

2021 ◽  
Vol 13 (16) ◽  
pp. 3169
Author(s):  
Michal Polák ◽  
Jakub Miřijovský ◽  
Alba E. Hernándiz ◽  
Zdeněk Špíšek ◽  
Radoslav Koprna ◽  
...  
Keyword(s):  
Plant Growth ◽  
Agricultural Research ◽  
Lidar Measurement ◽  
Agricultural Field ◽  
High Throughput Analysis ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Cutting Out ◽  
Flight Level ◽  
Field Plots

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a 0.78 to 0.79 correlation with LiDAR measurement with manual validation (RMSE = 0.19) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

scholarly journals Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Edisa Lozić ◽  
Benjamin Štular
Keyword(s):  
Data Processing ◽  
Good Practice ◽  
Airborne Lidar ◽  
Archaeological Prospection ◽  
Hard Data ◽  
Cloud Processing ◽  
Archaeological Interpretation ◽  
Point Cloud Processing ◽  
Data Acquisition And Processing ◽  
Airborne Lidar Data

Airborne LiDAR is a widely accepted tool for archaeological prospection. Over the last decade an archaeology-specific data processing workflow has been evolving, ranging from raw data acquisition and processing, point cloud processing and product derivation to archaeological interpretation, dissemination and archiving. Currently, though, there is no agreement on the specific steps or terminology. This workflow is an interpretative knowledge production process that must be documented as such to ensure the intellectual transparency and accountability required for evidence-based archaeological interpretation. However, this is rarely the case, and there are no accepted schemas, let alone standards, to do so. As a result, there is a risk that the data processing steps of the workflow will be accepted as a black box process and its results as “hard data”. The first step in documenting a scientific process is to define it. Therefore, this paper provides a critical review of existing archaeology-specific workflows for airborne LiDAR-derived topographic data processing, resulting in an 18-step workflow with consistent terminology. Its novelty and significance lies in the fact that the existing comprehensive studies are outdated and the newer ones focus on selected aspects of the workflow. Based on the updated workflow, a good practice example for its documentation is presented.

scholarly journals IMPACT OF PLANT GROWTH INHIBITORS ON THE GROWTH AND YIELD OF SORGHUM

2021 ◽  
Vol 52 (6) ◽  
pp. 1431-1440
Author(s):  
N. M. Abood ◽  
E. M. SHALAL ◽  
M. I. HAMDAN
Keyword(s):  
Plant Growth ◽  
Dry Matter ◽  
Agricultural Research ◽  
Block Design ◽  
Growth And Yield ◽  
Control Treatment ◽  
Research Station ◽  
Dry Matter Yield ◽  
Growth Inhibitors ◽  
Plant Growth Inhibitors

Field experiment was carried out during the spring and fall seasons of 2019 at the Abu Ghraib Research Station of the Agricultural Research Office/ Ministry of Agriculture, was aimed to investigate the effect of plant growth inhibitors on growth and yield of several sorghum varieties .Randomized Complete Block Design within split plot arrangement with three replications was used. The main plot consists of three growth inhibitors (Cycocel, Ethiphon, and Mebiquat), which were added at stages six and eight leaf stage, in addition to the control treatment (distilled water only), the sub plot was included three cultivars (Mabrouk, Buhooth 70 and Giza 113). The results showed in both seasons significant interaction between cultivars and growth inhibitors in most of the studied traits. The plants of the variety Giza 113 sprayed with Ethiphon recorded the lowest period of reaching physiological maturity, the highest average dry matter yield, and the highest weight of 1000 grains (89.00 days, 17.32 ton ha-1, and 39.33 gm) respectively. The same variety recorded the highest content of chlorophyll in the leaves and the highest grain yield due to the effect of Mebiquat (49.50 spad, 3.93 ton ha-1. The cultivar Buhooth 70 with the effect of Cycocel achieved the highest average of dry matter yield of 33.27 ton ha-1.

scholarly journals Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads

2020 ◽  
Vol 12 (10) ◽  
pp. 1677 ◽  
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquin Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
Henrique Lorenzo
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Applicable Law ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Preventive Actions ◽  
Total Study Area ◽  
Processing Techniques ◽  
Flammable Material

The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.

scholarly journals Special Issue on “Terrestrial Laser Scanning”: Editors’ Notes

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4569
Author(s):  
Joan R. Rosell-Polo ◽  
Eduard Gregorio ◽  
Jordi Llorens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Special Issue ◽  
Alternative Technologies ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing

In this editorial, we provide an overview of the content of the special issue on “Terrestrial Laser Scanning”. The aim of this Special Issue is to bring together innovative developments and applications of terrestrial laser scanning (TLS), understood in a broad sense. Thus, although most contributions mainly involve the use of laser-based systems, other alternative technologies that also allow for obtaining 3D point clouds for the measurement and the 3D characterization of terrestrial targets, such as photogrammetry, are also considered. The 15 published contributions are mainly focused on the applications of TLS to the following three topics: TLS performance and point cloud processing, applications to civil engineering, and applications to plant characterization.

Sign in / Sign up

Export Citation Format

Share Document