Agriculture & Husbandry Solutions

Problem: excessive fuel consumption during exploitation of truck transport in an agricultural company.
Solution: we developed an AI system to plan cargo and transport routes. It drastically reduced the waiting time at pick-up points and the empty transportation time. We integrated the relevant module into the company’s ERP system. The planning process became transparent and efficient.

Result: fuel costs have dropped by 18 - 22%.

Problem: high consumption of pesticides.
Solution: implementing daily monitoring of the plants, soil, and environmental conditions allowed us to identify the key factors impacting the consumption of pesticides. Following that, we’ve created the mathematical model enabling forecasts and applied the elements of precision agriculture technology.

Result: the average consumption of pesticides is now reduced by 13 - 15%; the yields have increased by 7 - 10%.

Problem: high water consumption.
Solution: the network of detecting devices was set up on fields to evaluate water level and quality, soil moisture level, and meteorological parameters. We configured permanent connection channels along with an AI cloud risk management system. The water supply system was revamped.

Result: water consumption has dropped by 15 - 20% (depending on weather); rice ripens faster by 5 - 8%.

Problem: early diagnosis of diseases of livestock animals with the least possible use of wearable health trackers.
Solution: we developed a system of video analysis based on a neural network. Also, it was necessary to place contactless sensors of physiological parameters and behavior on farms and location areas of animals.

Result: severe disease and mortality among animals have been reduced by 25 - 30%.

Problem: early detection of diseases.
Solution: to identify sites of diseases, we’ve developed new algorithms based on neural networks and fuzzy logic. A system of permanent video and hardware monitoring was set up.

Result: the infection is now localized and neutralized very early, which increases the yields by 12 - 17%.

Problem: poor forecast of the final production volume.
Solution: we’ve teamed up with experts to develop a methodology, identifying key factors impacting the production growth. A network of sensor systems to analyze the physical and biochemical parameters of the environment was built. Algorithms of predictive analytics were created and tested.

Result: the forecast precision has grown from 65 - 70%
to 85 - 92%.