Mikhail Patrikeev
Papers
1
Total Citations
6
H-Index
1
About
Mikhail Patrikeev is a researcher at the intersection of artificial intelligence and precision agriculture, with a primary focus on automated disease detection in 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—where facilities spanning over 100,000 m² require rapid, non-invasive pathogen identification. Patrikeev’s major contribution lies in developing neural network architectures capable of classifying powdery mildew infections from visual data, enabling real-time monitoring without human intervention. This work directly supports the scalability and efficiency of modern greenhouses, reducing crop losses and pesticide use. While his citation count is still growing, the practical relevance of his research to industrial horticulture is notable, particularly as global food production shifts toward controlled environments. Patrikeev’s achievements include bridging deep learning with agronomic diagnostics, offering a template for automated plant health surveillance that could extend to other crops and pathogens. For students and researchers, his work exemplifies how AI can solve tangible problems in sustainable agriculture, making him a rising voice in the field of digital farming.
Research Focus
Key Achievements
Top Papers
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