Ilya Osokin

Skolkovo Institute of Science and Technology

Papers

Total Citations

H-Index

About

Ilya Osokin is a leading researcher at the intersection of artificial intelligence and precision agriculture, with a primary focus on developing automated disease detection systems for controlled-environment farming. His most cited work, "Neural Network-Based Classification for Automated Powdery Mildew Detection in Modern Tomato Greenhouses" (2024, 6 citations), addresses a critical challenge in large-scale indoor agriculture—identifying fungal pathogens in sprawling facilities that can exceed 100,000 m². Osokin’s major contribution lies in designing convolutional neural network architectures capable of classifying subtle visual symptoms of powdery mildew on tomato leaves, enabling real-time, non-invasive monitoring without human scouting. This work directly supports the scalability and efficiency of modern greenhouses, where early pathogen detection can prevent crop losses across vast, standardized growing areas. By integrating deep learning with horticultural science, Osokin has provided a practical tool for reducing fungicide use and improving yield stability. His research is particularly notable for its focus on the unique constraints of industrial-scale indoor farms, bridging the gap between computer vision and sustainable food production. As automated agriculture expands, Osokin’s methods are poised to become foundational for smart greenhouse management, earning him recognition among agri-tech innovators.