A system for automatic detection of potential landing sites for horizontally landing unmanned aerial vehicles

2018 ◽  
Author(s):  
Jakub Rosner ◽  
Damian Pęszor ◽  
Marcin Paszkuta ◽  
Kamil Wereszczyński ◽  
Konrad Wojciechowski ◽  
...  
2021 ◽  
Vol 90 ◽  
pp. 101692
Author(s):  
Daniel Trevisan Bravo ◽  
Gustavo Araujo Lima ◽  
Wonder Alexandre Luz Alves ◽  
Vitor Pessoa Colombo ◽  
Luc Djogbénou ◽  
...  

2021 ◽  
Vol 13 (19) ◽  
pp. 3841
Author(s):  
Krishna Neupane ◽  
Fulya Baysal-Gurel

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.


Author(s):  
A.A. Moykin ◽  
◽  
A.S. Medzhibovsky ◽  
S.A. Kriushin ◽  
M.V. Seleznev ◽  
...  

Nowadays, the creation of remotely-piloted aerial vehicles for various purposes is regarded as one of the most relevant and promising trends of aircraft development. FAU "25 State Research Institute of Chemmotology of the Ministry of Defense of the Russian Federation" have studied the operation features of aircraft piston engines and developed technical requirements for motor oil for piston four-stroke UAV engines, as well as a new engine oil M-5z/20 AERO in cooperation with NPP KVALITET, LLC. Based on the complex of qualification tests, the stated operational properties of the experimental-industrial batch of M-5z/20 AERO oil are generally confirmed.


2020 ◽  
Vol 79 (11) ◽  
pp. 985-995
Author(s):  
Valerii V. Semenets ◽  
V. M. Kartashov ◽  
V. I. Leonidov

Sign in / Sign up

Export Citation Format

Share Document