Determination of Spring Barley Lodging Area with Help of Unmanned Aerial Vehicle

2021 ◽  
pp. 247-257
Author(s):  
Rashid K. Kurbanov ◽  
Natalia I. Zakharova
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spring Barley ◽  
Aerial Vehicle

DETERMINATION OF THE DIRECTION TO THE OBJECT AND THE INTENDED MISS ON BOARD AN UNMANNED AERIAL VEHICLE

2018 ◽  
pp. 11-18
Author(s):  
G. G. Sebryakov ◽  
S. M. Muzhichek ◽  
A. A. Skrynnikov ◽  
V. I. Pavlov ◽  
O. V. Ermolin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Vehicle

scholarly journals Unmanned Aerial Vehicle (UAV) and Photogrammetric Technic for 3D Tsunamis Safety Modeling in Cilacap, Indonesia

2021 ◽  
Vol 11 (23) ◽  
pp. 11310
Author(s):  
Muhammad Yudhi Rezaldi ◽  
Ambar Yoganingrum ◽  
Nuraini Rahma Hanifa ◽  
Yoshiyuki Kaneda ◽  
Siti Kania Kushadiani ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
3D Modeling ◽  
Spatial Data ◽  
Three Dimensional ◽  
Megathrust Earthquakes ◽  
End Point ◽  
Aerial Vehicle ◽  
Particle Visualization ◽  
Evacuation Routes

Three-dimensional (3D) modeling of tsunami events is intended to promote tsunami safety. However, the developed 3D modeling methods based on Computational Fluid Dynamics and photorealistic particle visualization have some weaknesses, such as not being similar to the original environment, not measuring the wave’s end point, and low image accuracy. The method for 3D modeling of tsunamis that results from this research can fulfil those weaknesses because it has advantages, such as being able to predict the end point of waves, similar to the original environment, and the height and area of inundation. In addition, the method produces more detailed and sharper spatial data. Modeling in this research is conducted using Agisoft Metashape Professional software to a produce 3D orthomosaic from pictures taken with Unmanned Aerial Vehicle (UAV) technique or drone (photogrammetry), and 3ds max software is used for wave simulation. We take a sample of an area in Cilacap, Indonesia that was impacted by the 2006 southwest coast tsunamis and may be vulnerable to future big megathrust earthquakes and tsunamis. The results could be used to provide several benefits, such as the creation of evacuation routes and the determination of appropriate locations for building shelters.

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.

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