In Ashgabat, an interagency seminar was held as part of a joint project between Turkmenistan’s Ministry of Agriculture and the Food and Agriculture Organization of the United Nations (FAO), titled “Support to Strengthening National Crop Monitoring through Remote Sensing Technology in Turkmenistan.”
The event brought together representatives from scientific centers and universities in Ashgabat and Arkadag specializing in the application of space-based and geographic information systems (GIS technologies) in agriculture.
International experts from Hungary and Russia served as keynote speakers, demonstrating the potential of this new technology for Turkmenistan, along with its institutional and technical implementation to benefit agricultural production.
Maxim Gorgan, Land Tenure Officer from the FAO Regional Office in Europe and Central Asia, explained that the project’s practical component focuses on piloting innovative remote monitoring methods for crop vegetation status. These rely on multispectral satellite imagery and unmanned aerial vehicles (drones).
To establish the national monitoring system, several stages are planned. First, the development of technical specifications for a national electronic model of remote sensing, with the Ministry of Agriculture serving as both partner and beneficiary.
The digital platform will be enhanced with tools to facilitate collaboration between scientists and producers, as well as for storing and processing core documentation.
In the longer term, integration of data from the national land cadastre and agricultural land classifications is envisioned, along with preparation of technical documentation to scale up the technology nationwide.
Turkmenistan had already begun developing similar software well before the project’s launch. During the seminar, a live videoconference demonstration showcased the results of this earlier work.
The program underwent testing in Mary province starting in September 2024 during winter wheat sowing, continued through the summer 2025 wheat harvest, and included spring and autumn cotton planting and harvesting periods.
Testing covered 400 fields and employed four vegetation indices, tracking minimum, average, and maximum values as well as deviations. The model enables automated crop classification by decoding satellite images based on spectral reflectance tied to the chemical composition of living plant tissues. Vegetation dynamics are visualized through “curves” that capture peak chlorophyll content in plant parts and its decline as crops reach maturity.
Associate Professor Maxim Ivanov from the Department of Landscape Ecology at Kazan Federal University, a Candidate of Geographical Sciences, highlighted that combining multispectral satellite imagery, drone surveys, and machine learning algorithms—integrated into a unified digital platform—allows rapid acquisition of data on crop structure and condition. This enables quick detection of issues such as waterlogging, drying, diseases, and other problems, supporting timely management decisions to minimize losses and safeguard yields as a key element of national food security.
Leading researcher Artur Gafurov, also a Candidate of Geographical Sciences from Kazan Federal University, presented applications of unmanned aerial systems in agriculture. These include drones, aircraft equipped with scanners, sensors, thermal imagers, and other tools for sector-specific tasks.
For instance, multirotor drones can apply pesticides against diseases and pests, conduct aerial surveys, and detect rodent infestations.
While remote methods provide a broad overview and identify affected zones, on-site field verification remains essential for accurate disease diagnosis. Experts view the optimal future approach as integrating satellite data, ground checks, and drone-based monitoring.
For Turkmenistan’s national model, the use of globally available, easily configurable analytical platforms without heavy reliance on neural networks was recommended.
The Ministry of Agriculture emphasized that this FAO-supported project, like others in the partnership, contributes to building digital capacity, training personnel in modern electronic programming, and strengthening cybersecurity in the agricultural sector. ///nCa, 21 January 2026 (based on the materials from the Newspaper “Neutral Turkmenistan”, photo credit – KFU)