Russian Scientists Use AI to Spot Infection and Parasite Risks in Potatoes
Researchers in Russia have developed an AI-based method to detect infectious and parasitic threats to potato crops at an early stage—aiming to cut yield losses before damage becomes visible.
Russian scientists have unveiled a new artificial intelligence–driven approach to assessing phytosanitary risks in potato crops. The method, developed by researchers at the Federal Scientific Agroengineering Center VIM, uses AI and biophotonics to quantitatively evaluate the risk of infectious and parasitic diseases, according to the Ministry of Science and Higher Education of Russia.
The team built a multi-level software system that combines computer vision and machine learning. AI algorithms identify the plant, segment individual leaves, and detect disease symptoms directly from images—allowing researchers to flag problems long before they are obvious to the human eye.
From Detection to Prevention
At the core of the method are nature-inspired biophotonic technologies, which make it possible to assess plant health at the cellular and physiological levels. Using this approach, scientists ranked infection risks by assigning scores to different pathogens.
The system covers 46 disease agents, including viruses, bacteria, nematodes, and fungal pathogens. By quantifying these risks, the researchers aim to move plant protection from reactive treatment to proactive prevention.
Developers say the platform is designed for continuous monitoring, with data regularly updated—an important feature as new pathogen strains emerge. Over time, the method could form the basis for rapid forecasting of disease spread, helping farmers and agronomists intervene before infections threaten large portions of a harvest.
For agriculture, where timing often determines the difference between a healthy yield and major losses, the promise is clear: use AI not just to see what’s wrong—but to predict what’s coming next.
