TY - JOUR

T1 - Определение культуры сельскохозяйственных растений по данным дистанционного зондирования с применением методов искусственного интеллекта

AU - Митрофанова, Ольга Александровна

AU - Нин, Ся

AU - Митрофанов, Евгений Павлович

PY - 2025

Y1 - 2025

N2 - One of the important subtasks for estimating and forecasting yields is crop mapping. In recent years, remote sensing data has been actively used to solve it, which allows us to quickly obtain information about the state of fields, as well as artificial intelligence methods. The purpose of this work was to investigate the possibilities of using neural network methods to determine crops of agricultural plants based on remote sensing data. Two different data sets are taken as a basis: an open dataset of PASTIS satellite images, as well as a mosaic of aerial photographs of the Agrophysical Research Institute obtained in the fields of the Leningrad region using the Geoscan-401 unmanned system. Five segmentation models (U-Net, U-Net 3+, DeepLabV3, FCN, Swin Transformer) were used for training and their performance was evaluated on a set of satellite image data. The results of the experiment showed that the accuracy of the U-Net 3+ and U-Net models significantly exceeds other models. At the same time, the transfer of models trained on low-resolution satellite images to high-resolution aerial photographs for further training has effectively improved the performance of models.

AB - One of the important subtasks for estimating and forecasting yields is crop mapping. In recent years, remote sensing data has been actively used to solve it, which allows us to quickly obtain information about the state of fields, as well as artificial intelligence methods. The purpose of this work was to investigate the possibilities of using neural network methods to determine crops of agricultural plants based on remote sensing data. Two different data sets are taken as a basis: an open dataset of PASTIS satellite images, as well as a mosaic of aerial photographs of the Agrophysical Research Institute obtained in the fields of the Leningrad region using the Geoscan-401 unmanned system. Five segmentation models (U-Net, U-Net 3+, DeepLabV3, FCN, Swin Transformer) were used for training and their performance was evaluated on a set of satellite image data. The results of the experiment showed that the accuracy of the U-Net 3+ and U-Net models significantly exceeds other models. At the same time, the transfer of models trained on low-resolution satellite images to high-resolution aerial photographs for further training has effectively improved the performance of models.

KW - aerial photography

KW - crop mapping

KW - neural network models

KW - satellite imagery

UR - https://www.mendeley.com/catalogue/af36ca4f-8945-381b-a763-e6ebcdf7fe2b/

U2 - 10.21638/spbu10.2025.108

DO - 10.21638/spbu10.2025.108

M3 - статья

VL - 21

SP - 112

EP - 121

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

SN - 1811-9905

IS - 1

ER -