Multiclass recognition of crops by a recurrent deep learning neural network with convolutional layers based on high-resolution color aerial photographs

Kirill Tokarev; Nikita Lebed

doi:10.55186/25876740_2024_67_2_192

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MULTICLASS RECOGNITION OF CROPS BY A RECURRENT DEEP LEARNING NEURAL NETWORK WITH CONVOLUTIONAL LAYERS BASED ON HIGH-RESOLUTION COLOR AERIAL PHOTOGRAPHS Multiclass recognition of crops by a recurrent deep learning neural network with convolutional layers based on high-resolution color aerial photographs

АА Аа

Published in INTERNATIONAL AGRICULTURAL JOURNAL · Issue 2 · Pages 192–195 · Rubric: Scientific support and management of agrarian and industrial complex

DOI: https://doi.org/10.55186/25876740_2024_67_2_192

Received: 10.10.2023 Accepted: 15.11.2023 Published: 15.04.2024

Authors

Kirill Tokarev

ORCID Scopus ResearcherID

Nikita Lebed

ORCID Scopus ResearcherID

Kirill Tokarev

Academic degree: candidate of economic sciences

Science rang: docent

Volgograd State Agricultural University associate professor of the department · department of mathematical modeling and informatics · Russian Federation · Volgograd

ORCID Scopus ResearcherID

Nikita Lebed

Academic degree: doctor of technical sciences

Volgograd State Agricultural University professor of the department · department of power supply and energy systems · Russian Federation · Volgograd

ORCID Scopus ResearcherID

ABSTRACT

The relevance of the study is due to the need to create an intelligent system for monitoring the state of crops during the growing season on significant areas. The scientific problem of operational monitoring of the state of crops with subsequent recognition of their condition from color images obtained from unmanned aerial vehicles (UAVs) can be solved by means of neural network algorithms of deep learning that provide analysis and processing of graphical information. As part of the computer implementation of a recurrent neural network with convolutional layers, the authors have formed a dataset of color images of crops obtained using UAVs. In order to solve the problem of class imbalance of the resulting dataset, its augmentation was carried out by artificially adding new images obtained on the basis of existing ones. For the training of a recurrent neural network implemented in Python, including convolutional layers, training and test samples were formed, with the marking of images in four classes: healthy vegetation ("healthy"), affected vegetation ("affected"), soil, unseeded field ("soil") and other objects ("other"). The recognition results obtained in the course of the study can be used to create hybrid architectures with subsequent software implementation of a complex of neural network models that allow identifying patterns of growth and development of various groups of crops.

KEYWORDS

multiclass recognition, artificial intelligence, phytoagrocenosis, productivity, convolutional neural networks

Funding

The study was supported by the Russian Science Foundation grant No. 22-21-20041, https://rscf.ru/project/22-21-20041/ and the Volgograd region.

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Tokarev K. E., Lebed N. I. Multiclass recognition of crops by a recurrent deep learning neural network with convolutional layers based on high-resolution color aerial photographs // INTERNATIONAL AGRICULTURAL JOURNAL. 2026. no. 2. pp. 192-195. DOI: https://doi.org/10.55186/25876740_2024_67_2_192. (Date of access 09.10.2026).

MLA

Kirill Tokarev, Lebed N. I.. "Multiclass recognition of crops by a recurrent deep learning neural network with convolutional layers based on high-resolution color aerial photographs." INTERNATIONAL AGRICULTURAL JOURNAL.2 (2026): 192-195. DOI: https://doi.org/10.55186/25876740_2024_67_2_192.

APA

Tokarev K. E., Lebed N. I. (2026). Multiclass recognition of crops by a recurrent deep learning neural network with convolutional layers based on high-resolution color aerial photographs. INTERNATIONAL AGRICULTURAL JOURNAL(2), 192-195. https://doi.org/10.55186/25876740_2024_67_2_192

Chicago

Kirill Tokarev, Lebed N. I.. "Multiclass recognition of crops by a recurrent deep learning neural network with convolutional layers based on high-resolution color aerial photographs." INTERNATIONAL AGRICULTURAL JOURNAL, no. 2 (2026): 192-195. https://doi.org/10.55186/25876740_2024_67_2_192.

WIKI

{{cite journal | author = Tokarev K. E., Lebed N. I. | title = Multiclass recognition of crops by a recurrent deep learning neural network with convolutional layers based on high-resolution color aerial photographs | journal = INTERNATIONAL AGRICULTURAL JOURNAL | year = 2026 | issue = 2 | pages = 192-195 | doi = 10.55186/25876740_2024_67_2_192 }}

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