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.

UAV Remote Sensing Assessment of Crop Growth

2021 ◽  
Vol 87 (12) ◽  
pp. 891-899
Author(s):  
Freda Elikem Dorbu ◽  
Leila Hashemi-Beni ◽  
Ali Karimoddini ◽  
Abolghasem Shahbazi
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Growth ◽  
Infrared Sensors ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Growth Indicators

The introduction of unmanned-aerial-vehicle remote sensing for collecting high-spatial- and temporal-resolution imagery to derive crop-growth indicators and analyze and present timely results could potentially improve the management of agricultural businesses and enable farmers to apply appropriate solution, leading to a better food-security framework. This study aimed to analyze crop-growth indicators such as the normalized difference vegetation index (NDVI), crop height, and vegetated surface roughness to determine the growth of corn crops from planting to harvest. Digital elevation models and orthophotos generated from the data captured using multispectral, red/green/blue, and near-infrared sensors mounted on an unmanned aerial vehicle were processed and analyzed to calculate the various crop-growth indicators. The results suggest that remote sensing-based growth indicators can effectively determine crop growth over time, and that there are similarities and correlations between the indicators.

scholarly journals Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat

2019 ◽  
Vol 11 (22) ◽  
pp. 2667 ◽  
Author(s):  
Jiang ◽  
Cai ◽  
Zheng ◽  
Cheng ◽  
Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Digital Cameras ◽  
Status Monitoring ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
N Status

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.

scholarly journals Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Qian Sun ◽  
Lin Sun ◽  
Meiyan Shu ◽  
Xiaohe Gu ◽  
Guijun Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Confusion Matrix ◽  
Accurate Determination ◽  
Vegetation Indices ◽  
Texture Features ◽  
Multispectral Image ◽  
Visual Interpretation ◽  
Aerial Vehicle

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

scholarly journals Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1023 ◽  
Author(s):  
Paolo Cinat ◽  
Salvatore Filippo Di Gennaro ◽  
Andrea Berton ◽  
Alessandro Matese
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Management ◽  
Computation Time ◽  
Technical Aspect ◽  
Targeted Intervention ◽  
Stable Algorithm ◽  
Management Procedure ◽  
Digital Elevation ◽  
Elevation Model

Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications.

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 Creation of the digital elevation model with the use of unmanned aerial vehicle

2019 ◽  
Vol 49 (3) ◽  
pp. 70-78
Author(s):  
A. I. Pavlova ◽  
V. K. Kalichkin ◽  
A. V. Kalichkin
Keyword(s):  
Digital Elevation Model ◽  
Unmanned Aerial Vehicle ◽  
Aerial Photography ◽  
Statistical Processing ◽  
Satellite Measurements ◽  
Support Points ◽  
Novosibirsk Region ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model

The necessary sequence of stages has been developed and the unmanned technology for creating a digital elevation model by the example of the land use of Novosibirsk region has been implemented. The technology consists of a set of stages: reconnaissance of the terrain, fi xing reference signs, satellite measurements, aerial photography fl ights, processing the results of aerial photography and the construction of digital elevation model. The technological process was signifi cantly affected by unfavorable weather conditions - low clouds, gusty wind, high air humidity. Remote sensing study with the use of unmanned aerial vehicle of the Supercam S 250 F type made it possible to create a large-scale orthophotoplan and a digital elevation model on the farm territory (M 1 : 1000). For photogrammetric processing of digital data obtained on the farm, a two-stage method of satellite determination was used. The essence of this method was to obtain a large number of satellite measurements in a static mode and further statistical processing. For statistical processing of satellite measurements, information was used on the coordinate location of two base ground stations of the Novosibirsk Region satellite network - Kochenevo and Novosibirsk. Remoteness of support points from the ground satellite station of Novosibirsk was at a distance of over 90 km. As a result of equalization calculations, the obtained average square displacement errors of the planned and high-altitude position of the support points in various test sites were under 0.02 m in the plan, and under 0.03 m by height. In the process of photogrammetric processing of the results of aerial photography with the use of unmanned aerial vehicle, the tasks of transferring the position of points on a digital image in the pixel coordinate system into the coordinate system of the area, building digital irregular (TIN, Triangulated Irregular Network) and regular (DEM, Digital Elevation Model) surface models, and based on them, textured terrain models (TTM, Textured Terrain Model) and orthophotoplans, were solved.

Assessing UAV Landslide Mapping Using Unmanned Aerial Vehicle (UAV) for Landslide Mapping Activity

2014 ◽  
Vol 567 ◽  
pp. 669-674 ◽  
Author(s):  
Munirah Radin Mohd Mokhtar ◽  
Abdul Nasir Matori ◽  
Khamaruzaman Wan Yusof ◽  
Abdul Mutalib Embong ◽  
Muhammad Ikhwan Jamaludin
Keyword(s):  
Digital Elevation Model ◽  
Unmanned Aerial Vehicle ◽  
Three Dimensional ◽  
Research Area ◽  
3D Surface ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Landslide Mapping ◽  
Elevation Model ◽  
Digital Orthophoto

The purpose of this research is to improve the landslide mapping using unmanned aerial vehicle (UAV) for the area of slope displacement. It further presents the UAV namely multi-motor that being used to capture images at the research area located in Parit, Perak. The objective of this research paper is to develop a three dimensional of landslide area produced from the UAV images. For the whole process of image processing, thirty six control points are established using global positioning system (GPS) staic mehtod using Agisoft Photoscan. The results show that the digital elevation model (DEM), aspect Model, slope model, and digital orthophoto can be obtained using the procedure and method used in the study. The information is obtained through accurate assessment results and used to create a 3D model which is then used to monitor technique applications. The restitution stereo model is also by three dimensional rotations or transformation in 3D surface. From here, the landslide can be detected by calculation of three difference epoch data achieved from Digital Elevation Model (DEM) generation. Prior to that, this paper focuses on the monitoring of that area based on DEM area and volume generated from 3D surface analysis. To conclude this study shows that UAV can be used for producing digital map.

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