Machine learning for remote monitoring of agricultural fields with explosive tunnels

Authors

  • A. Dudnyk National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • O. Opryshko National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • N. Kiktev National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • Y. Tsitsyurskii National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • D. Zhuk National University of Life and Environmental Sciences of Ukraine image/svg+xml

DOI:

https://doi.org/10.31548/energiya4(74).2024.075

Abstract

The article is devoted to the study of the application of neural networks for recognizing craters resulting from explosions in agricultural fields using satellite or UAV images.

The aim of the study is to assess the state of destruction based on data on the real state of fields obtained as a result of remote monitoring, using machine learning tools.

Monitoring in known works was carried out using satellite data with a high resolution of 0.3 - 0.5 m/pixel, but starting from 2022, such a Google Earth Pro product stopped being distributed for the territory of Ukraine. An alternative to obtaining such data can be UAVs, which, when using aircraft platforms, can survey up to 10,000 hectares per day.

It has been established that recognition of craters from explosions in images with a resolution of 0.5 m/ pixel is possible using neural networks. In the course of the study, round craters were studied, but during military operations other shapes are also possible, which require further study. In further studies, it is advisable to introduce an additional parameter - the crater shape index. The results obtained indicate the prospects for introducing this approach in the post-war restoration of agricultural lands in Ukraine.

 Key words: neural network, agricultural land, image recognition, blast craters, training, datase

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Published

2024-12-08

Issue

No. 4 (2024)

Section

Статті

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