Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026. Т. 23. № 3. С. 9-24
Artificial intelligence and remote sensing in agriculture: A review
A.N. Safonova 1 , Yu.A. Maglinets 2 , D.I. Kaplun 1 1 Saint Petersburg Electrotechnical University, Saint Petersburg, Russia
2 Siberian Federal University, Krasnoyarsk, Russia
Accepted: 24.10.2025
DOI: 10.21046/2070-7401-2026-23-3-9-24
In the third decade of the 21st century, the development of artificial intelligence has enabled algorithms, particularly those based on deep learning (DL) models, to take a leading position in the analysis of remote sensing data for agromonitoring. This advancement has allowed for improved accuracy and efficiency in tasks such as object detection and classification, change analysis, etc. Approximately one thousand articles have been published on this topic, including around 69 review papers. However, none has attempted to cover the entire spectrum of achievements in DL applications with remote sensing data in agromonitoring. The aim of this review is to identify the main trends and knowledge gaps in the development of remote agromonitoring applications by examining relevant review papers. Following recommended guidelines, 37 review studies published from 2020 to March 2024 were identified. The selected reviews discuss various agromonitoring tasks and methods for their resolution, including aspects such as DL architectures, data sources, model performance evaluation, and emerging challenges. Using bibliometric analysis, this study reveals common themes, highlights DL models performance, knowledge gaps, and development trends in this field. To the best of our knowledge, we present the most comprehensive review of this area and outline future research directions.
Keywords: artificial intelligence, deep learning, remote sensing, agromonitoring, agriculture, review
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