Harvesters in Russia Are Learning to Adapt to Different Crops
New digital modeling tech lets combines adjust to barley, rapeseed, and flax—cutting grain loss and slashing fuel and repair costs for farmers.
Researchers at Don State Technical University have developed a technology that allows grain harvesters to quickly and efficiently adapt to different crops. The system helps farmers harvest more carefully, reducing damage to grain and lowering expenses on fuel and maintenance. On a global scale, the Rostov team’s innovation feeds into the larger push toward “digital agriculture.” Creating so-called “digital twins” of farming equipment marks a shift away from universal machines toward precise, crop-specific tools that strengthen food security and agricultural resilience.
At the core of the innovation is a digital model of a harvester’s key component—the screw conveyor that moves grain inside the machine. The model incorporates the unique properties of each crop: flowability, density, and size. Engineers can then optimize the conveyor’s geometry at the design stage to fit specific tasks, whether it’s handling delicate flax, fine-seeded rapeseed, or barley.
For Russia’s agricultural sector, which must handle a vast range of crops across diverse climate zones, this technology could prove strategically vital.
The method has already been published in the peer-reviewed journal Engineering Technologies and Systems. It forms part of the university’s broader project to create a full-scale digital twin of a combine harvester—a development that could fundamentally transform the harvesting process in the years ahead.
