Selection of the optimal method for identifying agricultural land using neural networks

Dmitry Gura; Roman Dyachenko; A. M. Barysheva; A A Kolomytsev; V. V. Dovgal&#039;

doi:10.30533/GiA-2025-029

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SELECTION OF THE OPTIMAL METHOD FOR IDENTIFYING AGRICULTURAL LAND USING NEURAL NETWORKS Selection of the optimal method for identifying agricultural land using neural networks

АА Аа

Published in Izvestia Vuzov. Geodesy and Aerophotosurveying · Volume 69, Issue 2 · Pages 124–138 · Rubric: Land management, cadastre and land monitoring

DOI: https://doi.org/10.30533/GiA-2025-029

Received: 04.12.2024 Accepted: 23.04.2025 Published: 30.04.2025 Language of publication: RUS

Authors

Dmitry Gura

Roman Dyachenko

ORCID SPIN

A. M. Barysheva

A A Kolomytsev

et al.

V. V. Dovgal'

Dmitry Gura

Academic degree: candidate of technical sciences

Science rang: docent

Kuban State Technological University Associate Professor · Department of Cadastre and Geoengineering · Russian Federation · Krasnodar

Kuban State Agrarian University Associate Professor · Department of Geodesy · Russian Federation · Krasnodar

Roman Dyachenko

Academic degree: doctor of technical sciences

Science rang: professor

Kuban State Technological University Russian Federation

ORCID SPIN

A. M. Barysheva

A A Kolomytsev

Vserossiyskiy nauchnoissledovatel'skiy institut veterinarnoy virusologii i mikrobiologii RASHN

V. V. Dovgal'

ABSTRACT

The article presents the results of a study on the selection of an optimal neural network for identifying agricultural land plots, illustrated with vineyards, for updating the state agricultural land registry. The primary goal of the inventory is to detect any inconsistencies between the federal vineyard registry data and the vineyards’ current condition. For the experiments, the YOLOv5, YOLOv8, and Mask R-CNN neural networks were selected as the most commonly used for the purposes of recognizing objects in images. Neural networks offer advanced methods of image analysis that can be used for automated identification of vineyards on agricultural land. Their performance in the detection task was determined by calculating the Precision, Recall, and mAP metrics on a validation data set. The results of the comparison of the YOLOv5, YOLOv8, and Mask R-CNN models are presented in the table, which can be used to track their effectiveness. A comparison of the models showed that for a set of agricultural land plot images with vineyards, the YOLOv5 model may be preferable for tasks that require gradual improvement with an increasing number of epochs, while Mask R-CNN provides consistent high results even with a small number of epochs. YOLOv8 lags significantly in all metrics, especially in the early stages, and demonstrates the lowest overall performance.

KEYWORDS

agricultural land plots, land plot identification, vineyards, neural networks (NN/ANN), image detection, artificial intelligence, Mask R-CNN, YOLOv8, YOLOv5, metrics, mAP, Precision, Recall

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Gura D. A., Dyachenko R. A., Barysheva A. M., Kolomytsev A. A., Dovgal' V. V. Selection of the optimal method for identifying agricultural land using neural networks // Izvestia Vuzov. Geodesy and Aerophotosurveying. 2025. no. 2. pp. 124-138. DOI: https://doi.org/10.30533/GiA-2025-029. (Date of access 21.09.2026).

MLA

Dmitry Gura, Dyachenko R. A., Barysheva A. M., Kolomytsev A. A., Dovgal' V. V.. "Selection of the optimal method for identifying agricultural land using neural networks." Izvestia Vuzov. Geodesy and Aerophotosurveying 69.2 (2025): 124-138. DOI: https://doi.org/10.30533/GiA-2025-029.

APA

Gura D. A., Dyachenko R. A., Barysheva A. M., Kolomytsev A. A., Dovgal' V. V. (2025). Selection of the optimal method for identifying agricultural land using neural networks. Izvestia Vuzov. Geodesy and Aerophotosurveying, 69(2), 124-138. https://doi.org/10.30533/GiA-2025-029

Chicago

Dmitry Gura, Dyachenko R. A., Barysheva A. M., Kolomytsev A. A., Dovgal' V. V.. "Selection of the optimal method for identifying agricultural land using neural networks." Izvestia Vuzov. Geodesy and Aerophotosurveying 69, no. 2 (2025): 124-138. https://doi.org/10.30533/GiA-2025-029.

WIKI

{{cite journal | author = Gura D. A., Dyachenko R. A., Barysheva A. M., Kolomytsev A. A., Dovgal' V. V. | title = Selection of the optimal method for identifying agricultural land using neural networks | journal = Izvestia Vuzov. Geodesy and Aerophotosurveying | year = 2025 | volume = 69 | issue = 2 | pages = 124-138 | doi = 10.30533/GiA-2025-029 }}

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SELECTION OF THE OPTIMAL METHOD FOR IDENTIFYING AGRICULTURAL LAND USING NEURAL NETWORKS Selection of the optimal method for identifying agricultural land using neural networks

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