<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the last decade, the rate of the industrial usage of fixed-wing and blended wing aircraft has increased. A 1–2-km<sup>2</sup> area can be surveyed by such a drone within 30 to 60 minutes, without any special infrastructure, and this can be repeated at any time. This provides an opportunity to conduct automatized surveys and time series data testing, which can be used as a basis to decide specific processes. The state and the development of the plants can be monitored as well as the spread of pests and the efficiency of the procedures that protect against them. During the surveys, thousands of images are taken of the area, which can be converted to a georeferenced large-sized map within 20 to 40 hours, including post-production and a resolution varying from 0.01 to 0.1 cm/pixel. The paper provides a solution to the industrial post-production of these high-quantity data, in which a deep learning-based automated process using Matlab is presented, including a comparison of the results to the GIS data.</p></abstract>

In the last decade, the rate of the industrial usage of fixed-wing and blended wing aircraft has increased. A 1–2-km2 area can be surveyed by such a drone within 30 to 60 minutes, without any special infrastructure, and this can be repeated at any time. This provides an opportunity to conduct automatized surveys and time series data testing, which can be used as a basis to decide specific processes. The state and the development of the plants can be monitored as well as the spread of pests and the efficiency of the procedures that protect against them. During the surveys, thousands of images are taken of the area, which can be converted to a georeferenced large-sized map within 20 to 40 hours, including post-production and a resolution varying from 0.01 to 0.1 cm/pixel. The paper provides a solution to the industrial post-production of these high-quantity data, in which a deep learning-based automated process using Matlab is presented, including a comparison of the results to the GIS data.

Automated evaluation of agricultural damage using UAV survey

Institute of Applied Informatics, John von Neumann Faculty of Informatics

Acta Universitatis Sapientiae, Agriculture and Environment

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Automated evaluation of agricultural damage using UAV survey"}

In the last decade, the rate of the industrial usage of fixed-wing and blended wing aircraft has increased. A 1–2-km2 area can be surveyed by such a drone...

Automated evaluation of agricultural damage using UAV survey

Data publikacji: 23 lut 2019

Zakres stron: 20 - 30

Otrzymano: 20 sty 2018

Przyjęty: 28 lut 2018

DOI: https://doi.org/10.2478/ausae-2018-0002

Słowa kluczowe
deep learning, convolutional neural network, UAV, Matlab, survey, precision agriculture, game damage, time series

© 2018 Daniel Stojcsics et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Automated evaluation of agricultural damage using UAV survey

Data publikacji: 23 lut 2019

Zakres stron: 20 - 30

Otrzymano: 20 sty 2018

Przyjęty: 28 lut 2018

DOI: https://doi.org/10.2478/ausae-2018-0002

Słowa kluczowedeep learning, convolutional neural network, UAV, Matlab, survey, precision agriculture, game damage, time series

© 2018 Daniel Stojcsics et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
deep learning, convolutional neural network, UAV, Matlab, survey, precision agriculture, game damage, time series