Skoltech Digital Agro
Who we are
The smartest people work every day to provide the best agricultural service
Ivan Oseledets
Co-founder, CEO
Maria Pukalchik
Co-founder, CTO
Maxim Kuznetsov
Design Director
Ivan Khlebnikov
Marketing Director
TensorFields creates data solutions for crop-production farmers. Our Telegram @TensorFieldbot combines data mining and machine learning. Our process-based modeling estimates crop yield at a field level. As result, it solves agronomic problems like sowing data and irrigation plans.
Farmers provide simple data (field location, crop type, sowing date, irrigation, and fertilization days). Our expertise transforms this data into a high-value solution that advises farmers' actions. We solve the problem of low marginality in the agriculture business. The solution is developed with specialized AI technologies and includes weather forecast, soil properties, crop growing type in core. Finally, we provide +10% income for farmers annually.
Farmers provide simple data (field location, crop type, sowing date, irrigation, and fertilization days). Our expertise transforms this data into a high-value solution that advises farmers' actions. We solve the problem of low marginality in the agriculture business. The solution is developed with specialized AI technologies and includes weather forecast, soil properties, crop growing type in core. Finally, we provide +10% income for farmers annually.
New features for smart irrigation was tested
We propose a machine learning approach based on the crop simulation model WOFOST to assess the crop yield and water use efficiency. In our research, we use weather history to evaluate various weather scenarios. The application of multi-criteria optimization based on the non-dominated sorting genetic algorithm-II (NSGA-II) allows users to find the dates and volume of water for irrigation, maximizing the yield and reducing the total water consumption. In the study case, we compared the effectiveness of NSGA-II with the Monte Carlo search and a real farmer’s strategy. We showed a decrease in water consumption simultaneously with increased sugar-beet yield using the NSGA-II algorithm. Our approach yielded a higher potato crop than a farmer with a similar level of water consumption. The NSGA-II algorithm received an increase in yield for potato crops, but water use efficiency remained at the farmer’s level. NSGA-II used water resources more efficiently than the Monte Carlo search and reduced water losses to the lower soil horizons.
Our group is working to solve the problems of farmers
Experts in machine learning and soil science work together to solve farmers problems and improve crop yields
Feel free to contact us
Mikhail Gasanov
R&D
Email: mikhail.gasanov@skoltech.ru
