scholarly journals On the question of the main elements of precision agriculture in Kostanay region on the example of LLP “Agricultural Experimental Station “Zarechnoye”

2021 ◽  
Vol 211 (08) ◽  
pp. 11-17
Author(s):  
D. Zhamalova ◽  
Marat Tashmuhamedov
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Aerial Photography ◽  
Crop Production ◽  
Near Infrared ◽  
Agricultural Land ◽  
The State ◽  
Technical Program ◽  
Aerial Vehicle ◽  
Program Transfer

Abstract. The purpose of the research is to analyze the quality of sowing operations (flaws, sifting), the completeness of seedlings based on multispectral images. The research was carried out in accordance with the purpose of implementing the scientific and technical program “Transfer and adaptation of precision farming technologies in the production of crop production on the principle of "demonstration farms (landfills)” in Kostanay region" in 2019. Methods. To perform monitoring work, an unmanned aerial vehicle of an airplane type was used; a multispectral (MS) camera equipped with sensors of the main channels. Agrotechnical requirements have been developed taking into account the data of the electronic map of fields and the specifics of the region. The analysis of the state of crops using an information and analytical resource was carried out. Results. A survey of agricultural crops was conducted in order to obtain data on the state of the fields by an unmanned aerial vehicle. Aerial photography was performed with the Make sense Red-Edge multispectral camera at an altitude of 300 meters. The survey was carried out over 19 fields in five spectral ranges: blue, green, red, extreme red, near infrared. Aerial photography data are the initial data for the construction of orthophotoplanes, digital surface models, 3D-models. After conducting a flyby of the territory, the general condition of agricultural land was analyzed. Measurements are made on the reference fields using a portable device – an N-tester. The scientific novelty lies in the fact that aerial photography of spring wheat, which is at the stage of 3–4 leaves, was carried out, which revealed changes in the NDVI value, which during the ground survey confirmed an increase in the degree of clogging by annual millet weeds of the selected areas.

scholarly journals Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 27 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Small Scale ◽  
Area Of Interest ◽  
Sensing Applications ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model

Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

scholarly journals Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

2019 ◽  
Vol 11 (10) ◽  
pp. 1226 ◽  
Author(s):  
Jianqing Zhao ◽  
Xiaohu Zhang ◽  
Chenxi Gao ◽  
Xiaolei Qiu ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Similarity Index ◽  
Crop Growth ◽  
Contrast Threshold ◽  
Growth Monitoring ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Aerial Vehicle ◽  
Uav Images

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

scholarly journals Digitization of agricultural land using an unmanned aerial vehicle

2020 ◽  
Vol 548 ◽  
pp. 032002
Author(s):  
N V Abramov ◽  
S A Semizorov ◽  
S V Sherstobitov ◽  
M V Gunger ◽  
D A Petukhov
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Agricultural Land ◽  
Aerial Vehicle ◽  
Land Using

Natural superficial water storage and aquifer recharge assessment in Brazilian savanna wetland using unmanned aerial vehicle and geophysical survey

2020 ◽  
Vol 8 (3) ◽  
pp. 224-244
Author(s):  
Lucas Moreira Furlan ◽  
Vania Rosolen ◽  
Jepherson Salles ◽  
César Augusto Moreira ◽  
Manuel Eduardo Ferreira ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Agricultural Land ◽  
Water Volume ◽  
Surface Model ◽  
Aquifer Recharge ◽  
Flow Paths ◽  
Subsurface Structures ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Remote Sensing Techniques

Human pressure on the water resources provided by natural isolated wetlands has intensified in Brazil due to an increase in agricultural land equipped with irrigation. However, the amount of water stored in these areas and its contribution to aquifer recharge is unknown. This study aimed to quantify the amount of water that can be retained in a natural wetland and to propose a model of groundwater recharge. We used remote sensing techniques involving unmanned aerial vehicle to map the wetland and highlight its internal morphology, using a red–green–blue orthomosaic and a digital surface model. The 2-D inversion and a pseudo-3-D model from electrical resistivity tomography data were used to visualize the subsurface structures and hydrologic flow paths. The wetland is a reservoir storing up to 416.996 m3 of water during the rainy months. Distinct internal compartments characterize the wetland topography and different water-volume storage, lower in the border and higher in the center. A leakage point connects surface water to groundwater through direct vertical flow, which constitutes the aquifer recharge zone. Remotely sensed very high-resolution images allied with geophysical techniques allowed complete surface and subsurface imaging and offered visual tools that contributed to understanding the hydrodynamics of the wetland.

scholarly journals Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”

2020 ◽  
Vol 12 (6) ◽  
pp. 940 ◽  
Author(s):  
Xiuliang Jin ◽  
Zhenhai Li ◽  
Clement Atzberger
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Crop Production ◽  
Unmanned Ground Vehicle ◽  
Special Issue ◽  
Climate Conditions ◽  
Ground Vehicle ◽  
Global Food Security ◽  
3D Data ◽  
Potential Applications ◽  
Aerial Vehicle

High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.

Unmanned Aerial Systems Platform Research Prognosis

2012 ◽  
Vol 225 ◽  
pp. 555-560
Author(s):  
Javaan Chahl
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Systems Engineering ◽  
The State ◽  
Unmanned Aerial Systems ◽  
Research Opportunities ◽  
Engineering Approach ◽  
Current Trends ◽  
Research Investment ◽  
Aerial Vehicle ◽  
Aerial Systems

Much of aerospace academia is anticipating a boom in Unmanned Aerial Vehicle (UAV) funding and research opportunities. The expectation is built on the premise that UAVs will revolutionize aerospace, which is likely based on current trends. There is also an anticipation of an increasing number of new platforms and research investment, which is likely but must be analyzed carefully to determine where the opportunities might lie. This paper draws on the state of industry and a systems engineering approach. We explore what aspects of UAVs really are the results of aerospace science advances and what aspects will be rather more mundane works of engineering.

Methane emission detection and flux quantification from exploratory hydraulic fracturing in the United Kingdom, using unmanned aerial vehicle sampling

2020 ◽  
Author(s):  
Adil Shah ◽  
Hugo Ricketts ◽  
Joseph Pitt ◽  
Jacob Shaw ◽  
Khristopher Kabbabe ◽  
...  
Keyword(s):  
United Kingdom ◽  
Unmanned Aerial Vehicle ◽  
Gas Flow ◽  
Near Infrared ◽  
Near Field ◽  
Air Inlet ◽  
The United Kingdom ◽  
Aerial Vehicle ◽  
Flux Quantification ◽  
Intermittent Sampling

<p>Unmanned aerial vehicle (UAV) sampling was used to derive high-precision methane mole fraction measurements downwind of the United Kingdom’s first onshore exploratory operation to horizontally hydraulically fracture shale rock. Sampling took place using two UAVs on five intermittent sampling days between October 2018 and February 2019. One UAV carried an on-board prototype sensor while the other was connected to a sensor on the ground, using a tethered air inlet. Both instruments used near infrared spectroscopy. Methane emissions were observed on one sampling day (14<sup>th</sup> January 2019) over a 1.4-hour sampling window, due to cold venting of methane following a nitrogen lift. The nitrogen lift procedure was used to induce gas flow during liquid unloading. The near-field Gaussian plume inversion flux quantification method was used to derive four instantaneous flux ranges (within uncertainty) from the four UAV flight surveys conducted during the emission window.</p>

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