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Artificial Intelligence to Ensure Compliance with Estonia's Land Use Laws

8 minute read
The Estonian Agricultural Registers and Information Board (ARIB), like other European paying agencies, is responsible for the fair distribution of government subsidies to farmers and the verification of the subsidy claims.
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The Estonian Agricultural Registers and Information Board (ARIB), like other European paying agencies, is responsible for the fair distribution of government subsidies to farmers and the verification of the subsidy claims. The European Common Agricultural Policy (CAP) imposes laws that farmers follow to keep the land in good condition and qualify for government subsidies. Although non-compliance with these laws is very low, the overall economic impact on an EU level is major considering the size of the annual budget allocated to the CAP, which amounts to €50 billion. To validate claims for government subsidies, European paying agencies relied mainly on field inspectors.

The mowing of grassland is one of the main conditions for obtaining agricultural subsidies and this field recorded the highest level of non-compliance among Estonians. Indeed, manual inspection of all territories is not the best method in this case, as the last rounds of inspection covered only 5 to 6 percent of the land. Due to increased costs of human resources and the need to meet regulatory requirements, attention turned to an innovative solution that uses artificial intelligence to improve the inspection process and grant government subsidies to qualified farmers.

ARIB, Tartu Observatory, and CGI Estonia have collaborated to design the automated satellite-based infosystem SATIKAS. SATIKAS uses optical satellite imagery to check whether farmers are following the mowing requirement. The system operates from May to October every year to detect all mowing activities in Estonia. The system then processes the satellite images, sets dense time series, and uses over 150 radar and optical images. Moreover, SATIKAS identifies the characteristics of each land to follow mowing activities through state-of-the-art learning technologies.

SATIKAS employs learning methodologies and a convolutional neural network to analyze satellite data. The system analyzes optical satellite imagery along with reference data of farmer fields, historical inspection logs, and meteorological data from the Estonian Weather Service.

Results are published in real-time throughout the season in an open web map for all community members and in detailed reports for ARIB experts. The system reduces the expenses of costly field visits and saves time for inspectors by dispatching them directly to the fields that should be examined without wasting their time with complying lands. The system also reminds farmers to comply with mowing requirements by publishing the results on the web map. By exposing more non-compliance cases, SATIKAS reduces the number of payments made to non-eligible farmers, resulting in a €500,000 economic impact each year in Estonia alone. Based on the new system's need for further improvements and an increase in accuracy, SATIKAS has been equipped with a risk analysis tool, as of 2018, while gradually letting go of field visits and making payment decisions based on the SATIKAS satellite results. Ultimately, SATIKAS reduces EU expenditures and directs the freed resources to make further improvements brought about by technological development.

Although ARIB has been using SATIKAS since 2018, it originally started in 2011 as a research project by the Observatory of Tartu University. For development and implementation purposes, the system received funding from the European Regional Development Fund to design public services with information and communications technology. Public and private entities have collaborated to develop ARIB's system by sharing data, technological infrastructure, and machine learning expertise to store different datasets.

Public employees are now convinced of the potential of an AI-based system and have come to see its value and importance after they were initially skeptical of the project, fearing the creation of a Big Brother state or the disappearance of jobs. After the pilot was conducted and suitable trainings were given to employees, trust in AI system improved. Through training, employees discovered that they cannot fully rely on the system and that they should combine their expertise and its recommendations. Moreover, field inspectors were reassured that they will not lose their jobs, but that their nature would change due to the new system.

Mowing checks using satellite data are ARIB's first step towards a future that involves automated satellite-based monitoring and reduces the need for field visits. Plans are in motion to expand SATIKAS's functionality to include mowing and harvesting activity detection, crop classification, and flooded fields mapping. The accuracy of SATIKAS will be continuously enhanced to make payment decisions without the need for field visits. Next-generation satellites and other higher-resolution sensors will be used to improve the accuracy of subsidy checks and support farmers to develop their work through the information provided by the satellite.




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