Russian Scientists Have Taught a Neural Network to Predict Crop Yields
Russian researchers have developed a neural‑network system capable of forecasting crop yields with high accuracy, using big‑data analysis of soil conditions, weather patterns, and other agricultural factors
AI for Smarter Farming
Scientists at Kuban State Agrarian University have created a neural‑network tool designed to predict the yield of agricultural crops.
The system analyzes large datasets that include weather conditions, soil quality metrics, and additional environmental variables. Its purpose is to help farmers optimize fertilizer use, reduce business risks, and make more informed decisions throughout the growing season.
Early trials have demonstrated the system’s strong accuracy, and researchers are now working to adapt the model for practical deployment directly in the field. These improvements aim to increase reliability and accessibility for real‑world farm operations.
A Step Toward Full Agricultural Digitalization
The project represents another milestone in the digital transformation of Russian agriculture. During the recent Technoforge: AgroTECH hackathon, participants introduced several AI‑driven tools, including the EKO CONTROL platform for deep soil analysis and yield forecasting, a mobile app that uses artificial intelligence to evaluate seed quality, and another application capable of identifying plant phenological phases from images.
Together, these technologies illustrate how digital tools are rapidly reshaping farming practices—enabling more efficient resource use, greater sustainability, and stronger resilience to changing environmental conditions.
