scholarly journals Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4092 ◽  
Author(s):  
Xiongzhe Han ◽  
J. Alex Thomasson ◽  
G. Cody Bagnall ◽  
N. Ace Pugh ◽  
David W. Horne ◽  
...  
Keyword(s):  
Plant Height ◽  
Ground Truth ◽  
Ground Level ◽  
Strong Relationship ◽  
Future Application ◽  
Ground Control Points ◽  
Global Demand ◽  
Sorghum Plant ◽  
Aerial Vehicle ◽  
Uav Images

Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R2 > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R2 = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R2 = 0.99) existed between wind speed and image blurriness.

scholarly journals Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

2021 ◽  
Vol 13 (7) ◽  
pp. 1238
Author(s):  
Jere Kaivosoja ◽  
Juho Hautsalo ◽  
Jaakko Heikkinen ◽  
Lea Hiltunen ◽  
Pentti Ruuttunen ◽  
...  
Keyword(s):  
Reference Data ◽  
Measurement Techniques ◽  
Synthetic Data ◽  
Ground Truth ◽  
Precision Farming ◽  
Aerial Imaging ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

scholarly journals Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV): Norway Spruce Forest Case Study

2021 ◽  
Author(s):  
Veronika Kopačková-Strnadová ◽  
Lucie Koucká ◽  
Jan Jelenek ◽  
Zuzana Lhotakova ◽  
Filip Oulehle
Keyword(s):  
Norway Spruce ◽  
Unmanned Aerial Vehicle ◽  
Linear Models ◽  
Photosynthetic Pigment ◽  
Ground Truth ◽  
Needle Age ◽  
Vegetation Indexes ◽  
Pigment Contents ◽  
Aerial Vehicle ◽  
Uav Images

Remote sensing is one of the modern methods that have significantly developed over the last two decades and nowadays provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for accurate and timely monitoring of forests. In this study multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top and height as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge and NIR bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested i) to what extent can the linear relationship be established between selected vegetation indexes (NDVI and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents), and ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have a potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level, however the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the 2nd year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data acquired by the UAV platform at a very high spatial resolution.

scholarly journals Above-ground vegetation indices and yield attributes of rice crop using unmanned aerial vehicle combined with ground truth measurements

2020 ◽  
Vol 48 (4) ◽  
pp. 2385-2398
Author(s):  
Piyanan PIPATSITEE ◽  
Apisit EIUMNOH ◽  
Rujira TISARUM ◽  
Kanyarat TAOTA ◽  
Sumaid KONGPUGDEE ◽  
...  
Keyword(s):  
Plant Height ◽  
Unmanned Aerial Vehicle ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Grain Weight ◽  
Yield Traits ◽  
Aerial Vehicle ◽  
Ground Truthing ◽  
Non Destructive

Rice is an important economic and staple crop in several developing countries. Indica rice cultivars, ‘KDML105’ and ‘RD6’ are clear favourites, popular throughout world for their cooking quality, aroma, flavour, long grain, and soft texture, thus consequently dominate major plantation area in Northeastern region of Thailand. The objective of present study was to validate UAV (unmanned aerial vehicle)-derived information of rice crop traits with ground truthing non-destructive measurements in these rice varieties throughout whole life span under field environment. Plant height of cv. ‘KDML105’ was more than cv. ‘RD6’ for each respective stage. Whereas, number of tillers per clump in ‘KDML105’ exhibited stability at each developmental stage, which was in contrast to ‘RD6’ (increased continuously). Moreover, 1,000 grain weight, total grain weight and aboveground biomass were higher in ‘KDML105’ than in ‘RD6’ by 1.20, 1.82 and 3.82 folds. Four vegetative indices, ExG, EVI2, NDVI and NDRE derived from UAV platform proved out to be excellent parameters to compare KDML105 and RD6, especially in the late vegetative and reproductive developmental stages. Positive relationships between NDVI and NDRE, NDRE and total yield traits, as well as NDVI and aboveground biomass were demonstrated. In contrast, total chlorophyll pigment in cv. ‘RD6’ was higher than in cv. ‘KDML105’ leading to negative correlation with NDVI. ‘KDML105’ reflected rapid adaptation to Northeastern environments, leading to maintenance of plant height and yield components. Vegetation indices derived from UAV platform and ground truth non-destructive data exhibited high correlation. ‘KDML105’ was rapidly adapted to NE environments when compared with ‘RD6’, leading to maintenance of physiological parameters (detecting by UAV), the overall growth performances and yield traits (measuring by ground truth method). This study advocates harnessing and adopting the approach of UAV platform along with ground truthing non-destructive measurements of assessing a species/cultivars performance at broad land-use scale.

scholarly journals Automated Aerial Triangulation for UAV-Based Mapping

2018 ◽  
Vol 10 (12) ◽  
pp. 1952 ◽  
Author(s):  
Fangning He ◽  
Tian Zhou ◽  
Weifeng Xiong ◽  
Seyyed Hasheminnasab ◽  
Ayman Habib
Keyword(s):  
Bundle Adjustment ◽  
Superior Performance ◽  
Commercial Software ◽  
Aerial Triangulation ◽  
Ground Control Points ◽  
Estimated Parameters ◽  
Aerial Vehicle ◽  
Initial Recovery ◽  
Uav Images ◽  
Orientation Parameters

Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions.

scholarly journals Evaluating the Potential of PPK Direct Georeferencing for UAV-SfM Photogrammetry and Precise Topographic Mapping

2019 ◽  
Author(s):  
He Zhang ◽  
Emilien Aldana-Jague ◽  
François Clapuyt ◽  
Florian Wilken ◽  
Veerle Vanacker ◽  
...  
Keyword(s):  
Focal Length ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Time Period ◽  
Surface Models ◽  
Flight Height ◽  
Aerial Vehicle ◽  
Repeatability And Reproducibility ◽  
Uav Images ◽  
Topography Data

Abstract. Images captured by Unmanned aerial vehicle (UAV) and processed by Structure from Motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time- and cost-effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (Post-Processing Kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV-PPK-SfM workflow, we carried out multiple flight missions with different camera/UAV systems. Our analysis showed that the PPK solution provides the same accuracy (mean: ca. 0.01 m, RMSE: ca. 0.03 m) as the GCP method. Furthermore, our results indicated that camera properties (i.e., focal length, resolution, sensor quality) have an impact on the accuracy but planimetric and altimetric errors remained in the range of 0.011 to 0.024 m. By analysing the repeatability of DSM construction over a time period of a few months, our study demonstrates that a UAV-PPK-SfM workflow can provide consistent, repeatable 4D data with an accuracy of a few centimetres without the use of GCPs. An uncertainty analysis showed that the minimum level of topographical change detection was ca. ±0.04 m for a high-end DSLR camera and ca. ±0.08 m for an action camera (for a flight height of 45 m). The level of detection substantially improved when reducing the UAV flight height. This study demonstrates the repeatability, reproducibility and efficiency of a PPK-SfM workflow in the context of 4D earth surface monitoring with time-laps SfM photogrammetry. As such, it should be considered as an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

scholarly journals The Zurich urban micro aerial vehicle dataset

2017 ◽  
Vol 36 (3) ◽  
pp. 269-273 ◽  
Author(s):  
András L Majdik ◽  
Charles Till ◽  
Davide Scaramuzza
Keyword(s):  
Three Dimensional ◽  
Ground Truth ◽  
Ground Level ◽  
Urban Environments ◽  
Sensor Data ◽  
Measurement Unit ◽  
Reconstruction Algorithms ◽  
Ground Truth Data ◽  
Micro Aerial Vehicle ◽  
Aerial Vehicle

This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i.e. 5–15 m above the ground). The 2 km dataset consists of time synchronized aerial high-resolution images, global position system and inertial measurement unit sensor data, ground-level street view images, and ground truth data. The dataset is ideal to evaluate and benchmark appearance-based localization, monocular visual odometry, simultaneous localization and mapping, and online three-dimensional reconstruction algorithms for micro aerial vehicles in urban environments.

scholarly journals Rectification of Bowl-Shape Deformation of Tidal Flat DEM derived from UAV Imaging

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1602 ◽  
Author(s):  
Hyoseong Lee ◽  
Dongyeob Han
Keyword(s):  
Tidal Flat ◽  
Tidal Flats ◽  
Control Points ◽  
Matching Method ◽  
Height Difference ◽  
Ground Control Points ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Rotation Parameters ◽  
Uav Images

It is necessary to periodically obtain topographic maps of the geographical and environmental characteristics of tidal flats to systemically manage and monitor them. Accurate digital elevation models (DEMs) of the tidal flats are produced while using ground control points (GCPs); however, it is both complicated and difficult to conduct GPS surveys and readings of image coordinates that correspond to these because tidal flat areas are not easy to access. The position and distribution of GCPs affect DEMs, because the entire working area cannot be covered during a survey. In this study, a least-squares height-difference (LHD) DEM matching method with a polynomial model is proposed to increase the number of DEM grids while using a presecured precise DEM to rectify the distortion and bowl effect produced by unmanned aerial vehicle (UAV) images. The most appropriate result was obtained when the translation parameters were quadratic curve polynomials with an increasing number of grids and the rotation parameters were constant. The experimental results indicated that the proposed method reduced the distortion and eliminated the error caused by the bowl effect while only using a reference DEM.

scholarly journals Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

2019 ◽  
Vol 11 (17) ◽  
pp. 2021 ◽  
Author(s):  
Wei Su ◽  
Mingzheng Zhang ◽  
Dahong Bian ◽  
Zhe Liu ◽  
Jianxi Huang ◽  
...  
Keyword(s):  
Plant Height ◽  
Unmanned Aerial Vehicle ◽  
Estimation Error ◽  
Surface Model ◽  
Lodging Resistance ◽  
Height Estimation ◽  
Area Estimation ◽  
Corn Breeding ◽  
Aerial Vehicle ◽  
Uav Images

Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p < 0.001) and a root mean square error (RMSE) = 0.1677. In addition to a height estimation, the height variation was analyzed for depicting and validating the corn canopy uniformity stability for different varieties. For the lodging area estimation, the normalized DSM (nDSM) method was more promising than the gray-level co-occurrence matrix (GLCM) textural features method. The estimation error using the nDSM ranged from 0.8% to 5.3%, and the estimation error using the GLCM ranged from 10.0% to 16.2%. Associations between the height estimation and lodging area estimation were done to find the corn varieties with optimal plant heights and lodging resistance. For the LAI estimation, the physical radiative transfer PROSAIL model offered both an accurate and robust estimation performance both at the middle (R2 = 0.7490, RMSE = 0.3443) and later growing stages (R2 = 0.7450, RMSE = 0.3154). What was more exciting was that the estimated sequential time series LAIs revealed a corn variety with poor resistance to lodging in a study area of Baogaofeng Farm. Overall, UAVs appear to provide a promising method to support phenotyping for crop breeding, and the phenotyping of corn breeding in this study validated this application.

scholarly journals Generating a High-Precision True Digital Orthophoto Map Based on UAV Images

2018 ◽  
Vol 7 (9) ◽  
pp. 333 ◽  
Author(s):  
Yu Liu ◽  
Xinqi Zheng ◽  
Gang Ai ◽  
Yi Zhang ◽  
Yuqiang Zuo
Keyword(s):  
High Precision ◽  
Image Texture ◽  
Surface Model ◽  
Ground Sample ◽  
Planning And Design ◽  
Ground Control Points ◽  
Sensing Technology ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Digital Orthophoto

Unmanned aerial vehicle (UAV) low-altitude remote sensing technology has recently been adopted in China. However, mapping accuracy and production processes of true digital orthophoto maps (TDOMs) generated by UAV images require further improvement. In this study, ground control points were distributed and images were collected using a multi-rotor UAV and professional camera, at a flight height of 160 m above the ground and a designed ground sample distance (GSD) of 0.016 m. A structure from motion (SfM), revised digital surface model (DSM) and multi-view image texture compensation workflow were outlined to generate a high-precision TDOM. We then used randomly distributed checkpoints on the TDOM to verify its precision. The horizontal accuracy of the generated TDOM was 0.0365 m, the vertical accuracy was 0.0323 m, and the GSD was 0.0166 m. Tilt and shadowed areas of the TDOM were eliminated so that buildings maintained vertical viewing angles. This workflow produced a TDOM accuracy within 0.05 m, and provided an effective method for identifying rural homesteads, as well as land planning and design.

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