The challenge of reproducing remote sensing data from satellites and unmanned aerial vehicles (UAVs) in the context of management zones and precision agriculture

2020 ◽  
Author(s):  
Jesper Rasmussen ◽  
Saiful Azim ◽  
Søren Kjærgaard Boldsen ◽  
Thomas Nitschke ◽  
Signe M. Jensen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Precision Agriculture ◽  
Remote Sensing Data ◽  
Management Zones ◽  
Sensing Data ◽  
Aerial Vehicles

scholarly journals PERSPECTIVE OF USE OF REMOTE SENSING OF THE EARTH AND UNMANNED AERIAL VEHICLES IN THE AGRICULTURE OF TATARSTAN

2018 ◽  
Vol 12 (4) ◽  
pp. 17-19 ◽  
Author(s):  
Салават Сулейманов ◽  
Salavat Suleymanov ◽  
Николай Логинов ◽  
Nikolay Loginov
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Remote Sensing Data ◽  
Soil Characteristics ◽  
Sensing Data ◽  
Natural Processes ◽  
Aerial Vehicles ◽  
The Earth ◽  
Constant Change ◽  
Vast Territory

The vast territory of Russia, occupied by agricultural lands, is difficult to control due to the lack of an undeveloped network of operational monitoring points, ground stations, including meteorological stations, lack of aviation support due to the high cost of maintaining staff, etc. In addition, due to various types of natural processes, there is a constant change in the boundaries of acreage, soil characteristics and vegetation conditions in different fields and from site to site. Abroad, the above mentioned problems are successfully solved due to the application of remote sensing data (RSD) of the Earth, obtained with the help of unmanned aerial vehicles (UAVs). The proceedings, obtained (UAV), can help both to solve complex tasks of managing agricultural territories, and in highly specialized areas.

Implementation of several key functionalities of remote sensing data transmission for unmanned aerial vehicles

2015 ◽  
Vol 9 (1) ◽  
pp. 096032
Author(s):  
Haimeng Zhao ◽  
Shangqiang Yang ◽  
Peng Yang ◽  
Taixia Wu ◽  
Lei Yan
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Data Transmission ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Aerial Vehicles

scholarly journals Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

2017 ◽  
Vol 19 (4) ◽  
pp. 684-707 ◽  
Author(s):  
Claudia Georgi ◽  
Daniel Spengler ◽  
Sibylle Itzerott ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Site Specific ◽  
Management Zones ◽  
Sensing Data ◽  
Specific Management

scholarly journals Crop Monitoring using Unmanned Aerial Vehicles: A Review

2021 ◽  
Author(s):  
Jose Cuaran ◽  
Jose Leon
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Precision Agriculture ◽  
Environmental Changes ◽  
Great Opportunity ◽  
Crop Monitoring ◽  
Sensing Applications ◽  
Aerial Vehicles ◽  
Monitoring Applications ◽  
Common Application

Unmanned aerial vehicles (UAVs) or drones have been developed significantly over the past two decades, for a wide variety of applications such as surveillance, geographic studies, fire monitoring, security, military applications, search and rescue, agriculture, etc. In agriculture, for example, remote sensing by means of unmanned aerial vehicles has proven to be the most efficient way to monitor crops from images. Unlike remote sensing from satellite images or taken from manned aircraft, UAVs allow capturing images of high spatial and temporal resolution, thanks to their maneuverability and capability of flying at low altitude. This article presents an extensive review of the literature on crop monitoring by UAV, identifying specific applications, types of vehicles, sensors, image processing techniques, among others. A total of 50 articles related to crop monitoring applications of UAV in agriculture were reviewed. Only journal articles indexed in the Scopus database with more than 50 citations were considered. It was found that cereals are the most common crops where remote sensing has been applied so far. In addition, the most common crop remote sensing applications are related to precision agriculture, which includes the management of weeds, pests, diseases, nutrients and others. Crop phenotyping is also a common application of remote sensing, which consists of the evaluation of a crop’s physical characteristics under environmental changes, to select the plants or seeds with favorable genotype and phenotype. Besides, multirotor is the most common type of UAV used for remote sensing and RGB and multispectral cameras are mostly used as sensors for this application. Finally, there is a great opportunity for research in remote sensing related to a wide variety of crops, crop monitoring applications, vegetation indexes and photogrammetry.

scholarly journals A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

Agronomy ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 437 ◽  
Author(s):  
Piero Toscano ◽  
Annamaria Castrignanò ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Vittorio Vonella ◽  
Domenico Ventrella ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Durum Wheat ◽  
Precision Agriculture ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Farming Practices ◽  
Site Specific ◽  
Sensing Data ◽  
Yield Monitoring ◽  
Specific Management

The availability of big data in agriculture, enhanced by free remote sensing data and on-board sensor-based data, provides an opportunity to understand within-field and year-to-year variability and promote precision farming practices for site-specific management. This paper explores the performance in durum wheat yield estimation using different technologies and data processing methods. A state-of-the-art data cleaning technique has been applied to data from a yield monitoring system, giving a good agreement between yield monitoring data and hand sampled data. The potential use of Sentinel-2 and Landsat-8 images in precision agriculture for within-field production variability is then assessed, and the optimal time for remote sensing to relate to durum wheat yield is also explored. Comparison of the Normalized Difference Vegetation Index(NDVI) with yield monitoring data reveals significant and highly positive linear relationships (r ranging from 0.54 to 0.74) explaining most within-field variability for all the images acquired between March and April. Remote sensing data analyzed with these methods could be used to assess durum wheat yield and above all to depict spatial variability in order to adopt site-specific management and improve productivity, save time and provide a potential alternative to traditional farming practices.

scholarly journals Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

2016 ◽  
Author(s):  
Esteban Cano ◽  
Ryan Horton ◽  
Chase Liljegren ◽  
Duke M. Bulanon
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Unmanned Aerial Vehicles ◽  
Precision Agriculture ◽  
Farm Management ◽  
Rectilinear Motion ◽  
Straight Path ◽  
Aerial Vehicles ◽  
Management Technology ◽  
The Stability

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

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