Technology Development for Detecting Inhomogeneities in Agricultural Fields
Petr Skobelev1, Vitaly Travin2, Elena Simonova3, Vladimir Galuzin4, Anastasiya Galitsk5
1Petr Skobelev*, Professor, Department of Electronic Systems and Information Security, Samara State Technical University, Samara, Russia.
2Vitaly Travin, Project Manager, SEC «Smart Solutions», Ltd, Samara, Russia.
3Elena Simonova*, Ph.D. Associate Professor, Department of Information Systems and Technologies, Samara State Technical University, Samara, Russia.
4Vladimir Galuzin, Graduate Student, Department of Information Systems and Technologies, Samara State Technical University, Samara, Russia.
5Anastasiya Galitskaya, Graduate Student, Department of Information Systems and Technologies, Samara State Technical University, Samara, Russia.
Manuscript received on September 21, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3802-3808 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9830109119/2019©BEIESP | DOI: 10.35940/ijeat.A9830.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper addresses the relevant problem of identifying inhomogeneities in crop development based on analysis of multispectral images obtained from Earth remote sensing (ERS) satellites and unmanned aerial vehicles. Various techniques for detection of inhomogeneities are considered, which are based on computation of normalized difference vegetation index and its analysis, but do not take into account stages of crop production. The authors propose an original method and new algorithms for detecting inhomogeneities that take into account field zoning at various stages of the plant development cycle: preparing the field for sowing, emergence, and development of seedlings, heading. The paper also describes comparative analysis of results of the algorithms described in this paper and those of system-analogs. This analysis confirms effectiveness of the proposed algorithms. The method and algorithms for detection of inhomogeneities, developed by the authors, are used in the software module of image processing and presentation of ERS results for solving problems of precision farming, providing prompt, flexible, and efficient results for consumers.
Keywords: Crop production stages, Field inhomogeneity, Normalized Difference Vegetation Index, Precision agriculture, Remote sensing.