Russian Combine Harvesters Will Operate Without Human Operators
Russia is continuing to advance autonomous control technologies for agricultural machinery, relying on artificial intelligence and machine vision to enable fully driverless field operations.
AI and Computer Vision
In 2025, Russia launched practical field trials of an autonomous parallel-driving system for combine harvesters that uses computer vision and artificial intelligence components. The key advantage of this development is its ability to carry out harvesting autonomously even when communication quality or satellite positioning becomes unreliable.
Modern autonomous agricultural machinery traditionally depends on signals from global navigation systems such as GPS and GLONASS, which ensure high precision during field operations and harvesting. However, across Russia’s vast agricultural areas, signal quality can be inconsistent. The new domestic technology was developed specifically to address these conditions.
Trials are being conducted in Russia’s Krasnodar region on farms operated by the Progress Agro agricultural holding. Specialists from the holding are working jointly with researchers, engineers, and experienced mechanics from Kuban State Agrarian University. Their goal is to test the reliability and productivity of the new solution under the demanding conditions of real agricultural production.
Maximum Precision
The new system is built around machine vision. Video cameras mounted on the combine harvesters continuously capture the surrounding environment and transmit the footage to an AI system that processes the images in real time. The system accounts for terrain features, field boundaries, and other variables to generate the most efficient driving path. If the combine deviates from its route, automation returns the machine to the optimal trajectory.
Particular attention is being paid to the economic benefits of deploying the domestic solution, and early trial results have been positive.
By comparison, conventional GPS and GLONASS-based systems typically produce deviations of around 5–30 centimetres. The new AI and machine-vision-based technology reduces this margin to 5–10 centimetres. Compared with manual operation, traditional automated systems cut fuel consumption by about 12%. When AI and machine vision are applied, fuel savings increase to approximately 18%.
Payback periods are also improving. Investments in GPS and GLONASS navigation systems usually begin to pay off after two to three agricultural seasons. Under the new approach, the payback period is reduced to just one to two seasons.
Most importantly, when GPS and GLONASS signal quality deteriorates, the AI-based system becomes the only viable way to maintain autonomous operation of agricultural machinery.
The Future Belongs to Autonomous Systems
Current combine harvester trials represent only the first phase of large-scale deployment. Within a year, Russia is expected to introduce entire fleets of unmanned combine harvesters.
After harvesting trials are completed, developers plan to adapt the technology for tractors during sowing operations and later for other types of agricultural equipment.
Parallel-driving systems offer strong long-term potential because they significantly improve production efficiency. Fields are worked more precisely, with fewer gaps and overlaps, which has a positive impact on yields and reduces the consumption of seeds, fertilisers, and crop protection chemicals. Optimised routing lowers total machine mileage across fields, cutting fuel use and reducing maintenance and repair costs.
Agricultural operations can also be carried out at night or in poor visibility conditions, regardless of weather. All of this contributes to higher yields and, ultimately, increased profitability.
It is important to note that the new solution does not replace GPS and GLONASS, but complements them. The result is a comprehensive, fault-tolerant system for fully autonomous agricultural navigation. This is a fully Russian development, with operation and maintenance independent of foreign suppliers or contractors. In the longer term, Russia could export its autonomous agricultural machinery control systems to countries that need to cultivate remote or hard-to-access farmland.
